We have developed a framework, Cognitive ObjectRecognitionSystem (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to objectrecognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to objectrecognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

A method for objectrecognition using shape and color features of the object to be recognized. An adaptive architecture is used to recognize and adapt the shape and color features for moving objects to enable objectrecognition.

Automated video objectrecognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video objectrecognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The objectrecognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high objectrecognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video objectrecognition toward enabling practical low-power and mobile video processing

Automated video objectrecognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video objectrecognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The objectrecognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high objectrecognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video objectrecognition toward enabling practical low-power and mobile video processing

Sonars are used extensively in mobile robotics for obstacle detection, ranging and avoidance. However, these range-finding applications do not exploit the full range of information carried in sonar echoes. In addition, mobile robots need robust objectrecognitionsystems. Therefore, a simple and robust objectrecognitionsystem using ultrasonic sensors may have a wide range of applications in robotics. This dissertation develops and analyzes an objectrecognitionsystem that uses ultrasonic sensors of the type commonly found on mobile robots. Three principal experiments are used to test the sonar recognitionsystem: objectrecognition at various distances, objectrecognition during unconstrained motion, and softness discrimination. The hardware setup, consisting of an inexpensive Polaroid sonar and a data acquisition board, is described first. The software for ultrasound signal generation, echo detection, data collection, and data processing is then presented. Next, the dissertation describes two methods to extract information from the echoes, one in the frequency domain and the other in the time domain. The system uses the fuzzy ARTMAP neural network to recognize objects on the basis of the information content of their echoes. In order to demonstrate that the performance of the system does not depend on the specific classification method being used, the K- Nearest Neighbors (KNN) Algorithm is also implemented. KNN yields a test accuracy similar to fuzzy ARTMAP in all experiments. Finally, the dissertation describes a method for extracting features from the envelope function in order to reduce the dimension of the input vector used by the classifiers. Decreasing the size of the input vectors reduces the memory requirements of the system and makes it run faster. It is shown that this method does not affect the performance of the system dramatically and is more appropriate for some tasks. The results of these experiments demonstrate that sonar can be used to develop

Individuals with dyslexia often evince reduced activation during reading in left hemisphere (LH) language regions. This can be observed along with increased activation in the right hemisphere (RH), especially in areas associated with objectrecognition - a pattern referred to as RH compensation. The mechanisms of RH compensation are relatively unclear. We hypothesize that RH compensation occurs when the RH objectrecognitionsystem is called upon to supplement an underperforming LH visual word form recognitionsystem. We tested this by collecting ERPs while participants with a range of reading abilities viewed words, objects, and word/object ambiguous items (e.g., "SMILE" shaped like a smile). Less experienced readers differentiate words, objects, and ambiguous items less strongly, especially over the RH. We suggest that this lack of differentiation may have negative consequences for dyslexic individuals demonstrating RH compensation. PMID:25957504

Geometric and intensity features are very useful in objectrecognition. An intensity feature is a measure of contrast between object pixels and background pixels. Geometric features provide shape and size information. A model based approach is presented for computing geometric features. Knowledge about objects and imaging system is used to estimate orientation of objects with respect to the line of sight.

A knowledge-based 3D objectrecognitionsystem has been developed. The system uses the hierarchical structural, geometrical and relational knowledge in matching the 3D object models to the image data through pre-defined primitives. The primitives, we have selected, to begin with, are 3D boxes, cylinders, and spheres. These primitives as viewed from different angles covering complete 3D rotation range are stored in a "Primitive-Viewing Knowledge-Base" in form of hierarchical structural and relational graphs. The knowledge-based system then hypothesizes about the viewing angle and decomposes the segmented image data into valid primitives. A rough 3D structural and relational description is made on the basis of recognized 3D primitives. This description is now used in the detailed high-level frame-based structural and relational matching. The system has several expert and knowledge-based systems working in both stand-alone and cooperative modes to provide multi-level processing. This multi-level processing utilizes both bottom-up (data-driven) and top-down (model-driven) approaches in order to acquire sufficient knowledge to accept or reject any hypothesis for matching or recognizing the objects in the given image.

We describe a mobile vision system that is capable of automated object identification using images captured from a PDA or a camera phone. We present a solution for the enabling technology of outdoors vision based objectrecognition that will extend state-of-the-art location and context aware services towards object based awareness in urban environments. In the proposed application scenario, tourist pedestrians are equipped with GPS, W-LAN and a camera attached to a PDA or a camera phone. They are interested whether their field of view contains tourist sights that would point to more detailed information. Multimedia type data about related history, the architecture, or other related cultural context of historic or artistic relevance might be explored by a mobile user who is intending to learn within the urban environment. Learning from ambient cues is in this way achieved by pointing the device towards the urban sight, capturing an image, and consequently getting information about the object on site and within the focus of attention, i.e., the users current field of view.

Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with “fail-sating” or “mistake-proofing”. The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an objectrecognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of objectrecognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects. PMID:23966963

The automation process of the pattern recognition for fragments of objects is a challenge to humanity. For humans it is relatively easy to classify the fragment of some object even if it is isolated and perhaps this identification could be more complicated if it is partially overlapped by other object. However, the emulation of the functions of the human eye and brain by a computer is not a trivial issue. This paper presents a pattern recognition digital system based on Fourier binary rings masks in order to classify fragments of objects. The system is invariant to position, scale and rotation, and it is robust in the classification of images that have noise. Moreover, it classifies images that present an occlusion or elimination of approximately 50% of the area of the object.

The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

A knowledge-based three-dimensional (3D) objectrecognitionsystem is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

A knowledge-based three-dimensional (3D) objectrecognitionsystem is being developed. The system uses primitive-based hierarchical relational and structural matching for the recognition of 3D objects in the two-dimensional (2D) image for interpretation of the 3D scene. At present, the pre-processing, low-level preliminary segmentation, rule-based segmentation, and the feature extraction are completed. The data structure of the primitive viewing knowledge-base (PVKB) is also completed. Algorithms and programs based on attribute-trees matching for decomposing the segmented data into valid primitives were developed. The frame-based structural and relational descriptions of some objects were created and stored in a knowledge-base. This knowledge-base of the frame-based descriptions were developed on the MICROVAX-AI microcomputer in LISP environment. The simulated 3D scene of simple non-overlapping objects as well as real camera data of images of 3D objects of low-complexity have been successfully interpreted.

In this paper, a hybrid system of optics and computer for 3D objectrecognition is presented. The system consists of a Twyman-Green interferometer, a He-Ne laser, a computer, a TV camera, and an image processor. The structured light produced by a Twyman-Green interferometer is split in and illuminates objects in two directions at the same time. Moire contour is formed on the surface of object. In order to delete unwanted patterns in moire contour, we don't utilize the moire contour on the surface of object. We place a TV camera in the middle of the angle between two illuminating directions and take two groups of deformed fringes on the surface of objects. Two groups of deformed fringes are processed using the digital image processing system controlled and operated by XOR logic in the computer, moire fringes are then extracted from the complicated environment. 3D coordinates of points of the object are obtained after moire fringe is followed, and points belonging to the same fringe are given the same altitude. The object is described by its projected drawings in three coordinate planes. The projected drawings in three coordinate planes of the known objects are stored in the library of judgment. The object can be recognized by inquiring the library of judgment.

Attempts at fully automated objectrecognitionsystems have met with varying levels of success over the years. However, none of the systems have achieved high enough accuracy rates to be run unattended. One of the reasons for this may be that they are designed from the computer's point of view and rely mainly on image-processing methods. A better solution to this problem may be to make use of modern advances in computational intelligence and distributed processing to try to mimic how the human brain is thought to recognize objects. As humans combine cognitive processes with detection techniques, such a system would combine traditional image-processing techniques with computer-based intelligence to determine the identity of various objects in a scene.

System for optically recognizing and tracking a plurality of objects within a field of vision. Laser (46) produces a coherent beam (48). Beam splitter (24) splits the beam into object (26) and reference (28) beams. Beam expanders (50) and collimators (52) transform the beams (26, 28) into coherent collimated light beams (26', 28'). A two-dimensional SLM (54), disposed in the object beam (26'), modulates the object beam with optical information as a function of signals from a first camera (16) which develops X and Y signals reflecting the contents of its field of vision. A hololens (38), positioned in the object beam (26') subsequent to the modulator (54), focuses the object beam at a plurality of focal points (42). A planar transparency-forming film (32), disposed with the focal points on an exposable surface, forms a multiple position interference filter (62) upon exposure of the surface and development processing of the film (32). A reflector (53) directing the reference beam (28') onto the film (32), exposes the surface, with images focused by the hololens (38), to form interference patterns on the surface. There is apparatus (16', 64) for sensing and indicating light passage through respective ones of the positions of the filter (62), whereby recognition of objects corresponding to respective ones of the positions of the filter (62) is affected. For tracking, apparatus (64) focuses light passing through the filter (62) onto a matrix of CCD's in a second camera (16') to form a two-dimensional display of the recognized objects.

We study the issue of performance improvement of classification-based object detectors by including certain geometric-oriented filters. Configurations of the observed 3D scene may be used as a priori or a posteriori information for object filtration. A priori information is used to select only those object parameters (size and position on image plane) that are in accordance with the scene, restricting implausible combinations of parameters. On the other hand the detection robustness can be enhanced by rejecting detection results using a posteriori information about 3D scene. For example, relative location of detected objects can be used as criteria for filtration. We have included proposed filters in object detection modules of two different industrial vision-based recognitionsystems and compared the resulting detection quality before detectors improving and after. Filtering with a priori information leads to significant decrease of detector's running time per frame and increase of number of correctly detected objects. Including filter based on a posteriori information leads to decrease of object detection false positive rate.

The novel objectrecognition, or novel-object preference (NOP) test is employed to assess recognition memory in a variety of organisms. The subject is exposed to two identical objects, then after a delay, it is placed back in the original environment containing one of the original objects and a novel object. If the subject spends more time exploring one object, this can be interpreted as memory retention. To date, this test has not been fully explored in zebrafish (Danio rerio). Zebrafish possess recognition memory for simple 2- and 3-dimensional geometrical shapes, yet it is unknown if this translates to complex 3-dimensional objects. In this study we evaluated recognition memory in zebrafish using complex objects of different sizes. Contrary to rodents, zebrafish preferentially explored familiar over novel objects. Familiarity preference disappeared after delays of 5 mins. Leopard danios, another strain of D. rerio, also preferred the familiar object after a 1 min delay. Object preference could be re-established in zebra danios by administration of nicotine tartrate salt (50mg/L) prior to stimuli presentation, suggesting a memory-enhancing effect of nicotine. Additionally, exploration biases were present only when the objects were of intermediate size (2 × 5 cm). Our results demonstrate zebra and leopard danios have recognition memory, and that low nicotine doses can improve this memory type in zebra danios. However, exploration biases, from which memory is inferred, depend on object size. These findings suggest zebrafish ecology might influence object preference, as zebrafish neophobia could reflect natural anti-predatory behaviour. PMID:26376244

How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain’s visual system and suggest ways in which objectrecognition can be understood in terms of interactions within and between processes over time. PMID:23554596

Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

Recognition of target objects in remotely sensed imagery required detailed knowledge about the target object domain as well as about mapping properties of the sensing system. The art of objectrecognition is to combine both worlds appropriately and to provide models of target appearance with respect to sensor characteristics. Common approaches to support interactive objectrecognition are either driven from the sensor point of view and address the problem of displaying images in a manner adequate to the sensing system. Or they focus on target objects and provide exhaustive encyclopedic information about this domain. Our paper discusses an approach to assist interactive objectrecognition based on knowledge about target objects and taking into account the significance of object features with respect to characteristics of the sensed imagery, e.g. spatial and spectral resolution. An `interactive recognition assistant' takes the image analyst through the interpretation process by indicating step-by-step the respectively most significant features of objects in an actual set of candidates. The significance of object features is expressed by pregenerated trees of significance, and by the dynamic computation of decision relevance for every feature at each step of the recognition process. In the context of this approach we discuss the question of modeling and storing the multisensorial/multispectral appearances of target objects and object classes as well as the problem of an adequate dynamic human-machine-interface that takes into account various mental models of human image interpretation.

This invention describes a method for identifying and tracking an object from two-dimensional data pictorially representing said object by an object-tracking system through processing said two-dimensional data using at least one tracker-identifier belonging to the object-tracking system for providing an output signal containing: a) a type of the object, and/or b) a position or an orientation of the object in three-dimensions, and/or c) an articulation or a shape change of said object in said three dimensions.

Compton Backscatter Imaging (CBI) has always been impeded by inefficient sensing of information carrying photons, and extensive structured noise due to object surface features and heterogeneity. In this research project, a new variant of CBI, which substantially resolves both impedimental is suggested, developed and rigorously tested by application to a difficult imaging problem. The new approach is termed Lateral Migration Radiography (LMR) which aptly describes the specific photon history process giving rise to resulting image contrast. The photons employed in this research are conventionally generated x rays. A pencil x-ray beam with a typical filtered-bremsstrahlung photon energy spectrum is perpendicularly incident upon, and systematically rastered over, the object to be imaged. Efficient sensing of information-carrying photons is achieved by employing large-area detectors with sensitive planes perpendicular to the incident beam. A geometric array of a group of such detectors along with varying degrees of detector collimation to discriminate singly-scattered from multiple-scattered, detected x rays is developed. The direct output of the detector-array components is algebraically combined to eliminate image cloaking by surface features and heterogeneity. Image contrast is generated by the variation of x-ray interaction probabilities in the internal details relative to the surrounding material. These major improvements to conventional CBI have allowed the detection of internals with clarity such that recognition of the internal features via the image details is possible in cases where ordinary CBI can not even detect the presence of the internal structure. The test application is the detection and recognition of all-plastic antitank landmines buried in soil at depths of up to three inches. In the military application of clearing 12 inch diameter mines from 14-foot-wide tank-lanes, the spatial resolution requirement of one inch and the speed of 3 to 5 mph over

This work concerns computationally efficient computer vision methods for the search for and identification of small objects in large images. The approach combines neural network pattern recognition with pyramid-based coarse-to-fine search, in a way that eliminates the drawbacks of each method when used by itself and, in addition, improves object identification through learning and exploiting the low-resolution image context associated with the objects. The presentation will describe the system architecture and the performance on illustrative problems.

Whether hiding from predators, or avoiding battlefield casualties, camouflage is widely employed to prevent detection. Disruptive coloration is a seemingly well-known camouflage mechanism proposed to function by breaking up an object's salient features (for example their characteristic outline), rendering objects more difficult to recognize. However, while a wide range of animals are thought to evade detection using disruptive patterns, there is no direct experimental evidence that disruptive coloration impairs recognition. Using humans searching for computer-generated moth targets, we demonstrate that the number of edge-intersecting patches on a target reduces the likelihood of it being detected, even at the expense of reduced background matching. Crucially, eye-tracking data show that targets with more edge-intersecting patches were looked at for longer periods prior to attack, and passed-over more frequently during search tasks. We therefore show directly that edge patches enhance survivorship by impairing recognition, confirming that disruptive coloration is a distinct camouflage strategy, not simply an artefact of background matching. PMID:24152693

A fast 3-D objectrecognition algorithm that can be used as a quick-look subsystem to the vision system for the Special-Purpose Dexterous Manipulator (SPDM) is described. Global features that can be easily computed from range data are used to characterize the images of a viewer-centered model of an object. This algorithm will speed up the processing by eliminating the low level processing whenever possible. It may identify the object, reject a set of bad data in the early stage, or create a better environment for a more powerful algorithm to carry the work further.

Objectrecognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of objectrecognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model. PMID:17604954

It is now emerging that vision is usually limited by object spacing rather than size. The visual system recognizes an object by detecting and then combining its features. ‘Crowding’ occurs when objects are too close together and features from several objects are combined into a jumbled percept. Here, we review the explosion of studies on crowding—in grating discrimination, letter and face recognition, visual search, selective attention, and reading—and find a universal principle, the Bouma law. The critical spacing required to prevent crowding is equal for all objects, although the effect is weaker between dissimilar objects. Furthermore, critical spacing at the cortex is independent of object position, and critical spacing at the visual field is proportional to object distance from fixation. The region where object spacing exceeds critical spacing is the ‘uncrowded window’. Observers cannot recognize objects outside of this window and its size limits the speed of reading and search. PMID:18828191

Visual appearance of natural objects is profoundly affected by viewing conditions such as viewpoint and illumination. Human subjects can nevertheless compensate well for variations in these viewing conditions. The strategies that the visual system uses to accomplish this are largely unclear. Previous computational studies have suggested that in principle, certain types of object fragments (rather than whole objects) can be used for invariant recognition. However, whether the human visual system is actually capable of using this strategy remains unknown. Here, we show that human observers can achieve illumination invariance by using object fragments that carry the relevant information. To determine this, we have used novel, but naturalistic, 3-D visual objects called "digital embryos." Using novel instances of whole embryos, not fragments, we trained subjects to recognize individual embryos across illuminations. We then tested the illumination-invariant objectrecognition performance of subjects using fragments. We found that the performance was strongly correlated with the mutual information (MI) of the fragments, provided that MI value took variations in illumination into consideration. This correlation was not attributable to any systematic differences in task difficulty between different fragments. These results reveal two important principles of invariant objectrecognition. First, the subjects can achieve invariance at least in part by compensating for the changes in the appearance of small local features, rather than of whole objects. Second, the subjects do not always rely on generic or pre-existing invariance of features (i.e., features whose appearance remains largely unchanged by variations in illumination), and are capable of using learning to compensate for appearance changes when necessary. These psychophysical results closely fit the predictions of earlier computational studies of fragment-based invariant objectrecognition. PMID:22936910

Human visual objectrecognition is multifaceted and comprised of several domains of expertise. Developmental relations between young children's letter recognition and their 3-dimensional objectrecognition abilities are implicated on several grounds but have received little research attention. Here, we ask how preschoolers' success in recognizing…

How long does it take for the human visual system to recognize objects? This issue is important for understanding visual cortical function as it places constraints on models of the information processing underlying recognition. We designed a series of event-related potential (ERP) experiments to measure the timecourse of electrophysiological correlates of objectrecognition. We find two distinct types of components in the ERP recorded during categorization of natural images. One is an early presentation-locked signal arising around 135 ms that is present when there are low-level feature differences between images. The other is a later, recognition-related component arising between 150-300 ms. Unlike the early component, the latency of the later component covaries with the subsequent reaction time. In contrast to previous studies suggesting that the early, presentation-locked component of neural activity is correlated to recognition, these results imply that the neural signatures of recognition have a substantially later and variable time of onset. PMID:14507255

In this paper, a new method of 3-D objectrecognition using optical techniques and a computer is presented. We perform 3-D objectrecognition using moire contour to obtain the object's 3- D coordinates, projecting drawings of the object in three coordinate planes to describe it and using a method of inquiring library of judgement to match objects. The recognition of a simple geometrical entity is simulated by computer and studied experimentally. The recognition of an object which is composed of a few simple geometrical entities is discussed.

This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and objectrecognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant objectrecognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and objectrecognition that occur across infancy. PMID:25596333

One of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical objectrecognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian objectrecognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision--a very 'expert system' like capability. When various sources of incoming data are represented by C++ classes, it becomes possible to automatically backtrack the Bayesian data fusion process, assigning relative weights to the more significant datums and their combinations. A C++ object oriented engine is then able to synthesize 'English' like textural description of the Bayesian reasoning suitable for generalized presentation. Key concepts and examples are provided based on an actual objectrecognition problem.

What humans actually observe and how they comprehend this information is complex due to Gestalt processes and interaction of context in predicting the course of thinking and enforcing one idea while repressing another. How we extract the knowledge from the scene, what we get from the scene indeed and what we bring from our mechanisms of perception are areas separated by a thin, ill-defined line. The purpose of this paper is to present a system for Representing Knowledge and Recognizing and Interpreting Attention Trailed Entities dubbed as REKRIATE. It will be used as a tool for discovering the underlying principles involved in knowledge representation required for conceptual learning. REKRIATE has some inherited knowledge and is given a vocabulary which is used to form rules for identification of the object. It has various modalities of sensing and has the ability to measure the distance between the objects in the image as well as the similarity between different images of presumably the same object. All sensations received from matrix of different sensors put into an adequate form. The methodology proposed is applicable to not only the pictorial or visual world representation, but to any sensing modality. It is based upon the two premises: a) inseparability of all domains of the world representation including linguistic, as well as those formed by various sensor modalities. and b) representativity of the object at several levels of resolution simultaneously.

Human visual objectrecognition is multifaceted, with several domains of expertise. Developmental relations between young children's letter recognition and their 3-dimensional objectrecognition abilities are implicated on several grounds but have received little research attention. Here, we ask how preschoolers’ success in recognizing letters relates to their ability to recognize 3-dimensional objects from sparse shape information alone. A relation is predicted because perception of the spatial relations is critical in both domains. Seventy-three 2 ½- to 4-year-old children completed a Letter Recognition task, measuring the ability to identify a named letter among 3 letters with similar shapes, and a “Shape Caricature Recognition” task, measuring recognition of familiar objects from sparse, abstract information about their part shapes and the spatial relations among those parts. Children also completed a control “Shape Bias” task, in which success depends on recognition of overall object shape but not of relational structure. Children's success in letter recognition was positively related to their shape caricature recognition scores, but not to their shape bias scores. The results suggest that letter recognition builds upon developing skills in attending to and representing the relational structure of object shape, and that these skills are common to both 2-dimensional and 3-dimensional object perception. PMID:25969673

This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory. PMID

One crucial component of a control system for autonomous vehicle guidance is real time image analysis. This system part is burdened by the maximum flow of information. To overcome the high demands in computation power a combination of knowledge based scene analysis and special hardware has been developed. The use of knowledge based image analysis supports real time processing not by schematically evaluating all parts of the image, but only evaluating those which contain relevant information. This is due to the fact that in many practical problems the relevant information is very unevenly distributed over the image. Preknowledge of the problem or the aim of the mission and expectations or predictions about the scene sustantially reduce the amount of information to be processed. The operations during such an analysis may be divided into two classes - simple processes, e.g. filters, correlation, contour processing and simple search strategies - complex search and control strategy This classification supplied the concept for a special hardware. The complex tasks are performed by a universal processor 80286 while the remaining tasks are executed by a special coprocessor (including image memory). This combination permits the use of filter masks with a arbitrary geometry together with a powerful search strategy. A number of these basic modules may be configured into a multiprocessor system. The universal processor is programmed in a high level language. To support the coprocessor a set of software tools has been built. They permit interactive graphical manipulation of filtermasks, generation of simple search strategies and non real time simulation. Also the real data structures that control the function of the coprocessor are generated by this software package. The system is used within our autonomous vehicle project. One set of algorithms tracks the border lines of the road even if they are broken or disturbed by dirt. Also shadows of bridges crossing the road are

A model-based objectrecognition technique is introduced in this paper to identify and locate an object in any position and orientation. The test scenes could consist of an isolated object or several partially overlapping objects. A cooperative feature matching technique is proposed which is implemented by a Hopfield neural network. The proposed matching technique uses the parallelism of the neural network to globally match all the objects (they may be overlapping or touching) in the input scene against all the object models in the model-database at the same time. For each model, distinct features such as curvature points (corners) are extracted and a graph consisting of a number of nodes connected by arcs is constructed. Each node in the graph represents a feature which has a numerical feature value and is connected to other nodes by an arc representing the relationship or compatibility between them. Objectrecognition is formulated as matching a global model graph, representing all the object models, with an input scene graph representing a single object or several overlapping objects. A 2-dimensional Hopfield binary neural network is implemented to perform a subgraph isomorphism to obtain the optimal compatible matching features between the two graphs. The synaptic interconnection weights between neurons are designed such that matched features belonging to the same model receive excitatory supports, and matched features belonging to different models receive an inhibitory support or a mutual support depending on whether the input scene is an isolated object or several overlapping objects. The coordinate transformation for mapping each pair of matched nodes from the model onto the input scene is calculated, followed by a simple clustering technique to eliminate any false matches. The orientation and the position of objects in the scene are then calculated by averaging the transformation of correct matched nodes. Some simulation results are shown to illustrate the

In rodents, the novel objectrecognition task (NOR) has become a benchmark task for assessing recognition memory. Yet, despite its widespread use, a consensus has not developed about which brain structures are important for task performance. We assessed both the anterograde and retrograde effects of hippocampal lesions on performance in the NOR…

Discusses Needham's findings by asserting that they extend understanding of infant perception by showing that the memory representations infants draw upon have bound together information about shape, color, and pattern. Considers the distinction between two senses of "recognition" and asks in which sense objectrecognition contributes to object…

Key properties of inferior temporal cortex neurons are described, and then, the biological plausibility of two leading approaches to invariant visual objectrecognition in the ventral visual system is assessed to investigate whether they account for these properties. Experiment 1 shows that VisNet performs object classification with random exemplars comparably to HMAX, except that the final layer C neurons of HMAX have a very non-sparse representation (unlike that in the brain) that provides little information in the single-neuron responses about the object class. Experiment 2 shows that VisNet forms invariant representations when trained with different views of each object, whereas HMAX performs poorly when assessed with a biologically plausible pattern association network, as HMAX has no mechanism to learn view invariance. Experiment 3 shows that VisNet neurons do not respond to scrambled images of faces, and thus encode shape information. HMAX neurons responded with similarly high rates to the unscrambled and scrambled faces, indicating that low-level features including texture may be relevant to HMAX performance. Experiment 4 shows that VisNet can learn to recognize objects even when the view provided by the object changes catastrophically as it transforms, whereas HMAX has no learning mechanism in its S-C hierarchy that provides for view-invariant learning. This highlights some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform, in order to help understand the fundamental underlying principles of invariant visual objectrecognition in the ventral visual stream. PMID:26335743

A general body-wide automatic anatomy recognition (AAR) methodology was proposed in our previous work based on hierarchical fuzzy models of multitudes of objects which was not tied to any specific organ system, body region, or image modality. That work revealed the challenges encountered in modeling, recognizing, and delineating sparse objects throughout the body (compared to their non-sparse counterparts) if the models are based on the object's exact geometric representations. The challenges stem mainly from the variation in sparse objects in their shape, topology, geographic layout, and relationship to other objects. That led to the idea of modeling sparse objects not from the precise geometric representations of their samples but by using a properly designed optimal super form. This paper presents the underlying improved methodology which includes 5 steps: (a) Collecting image data from a specific population group G and body region Β and delineating in these images the objects in Β to be modeled; (b) Building a super form, S-form, for each object O in Β; (c) Refining the S-form of O to construct an optimal (minimal) super form, S*-form, which constitutes the (fuzzy) model of O; (d) Recognizing objects in Β using the S*-form; (e) Defining confounding and background objects in each S*-form for each object and performing optimal delineation. Our evaluations based on 50 3D computed tomography (CT) image sets in the thorax on four sparse objects indicate that substantially improved performance (FPVF~2%, FNVF~10%, and success where the previous approach failed) can be achieved using the new approach.

Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for objectrecognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69. PMID:26837672

Objectrecognition is an image processing task of finding a given object in a selected image or video sequence. Objectrecognition can be divided into two areas: one of these is decision-theoretic and deals with patterns described by quantitative descriptors, for example such as length, area, shape and texture. With this Graphical User Interface Circuitry (GUIC) methodology employed here being relatively new for objectrecognitionsystems, the aim of this work is to identify if the developed circuitry can detect certain shapes or strings within the target image. A much smaller reference image feeds the preset data for identification, tests are conducted for both binary and greyscale and the additional mathematical morphology to highlight the area within the target image with the object(s) are located is also presented. This then provides proof that basic recognition methods are valid and would allow the progression to developing decision-theoretical and learning based approaches using GUICs for use in multidisciplinary tasks.

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in objectrecognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For objectrecognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Real-time objectrecognition using a compact grayscale optical correlator will be introduced. A holographic memory module for storing a large bank of optimum correlation filters, to accommodate the large data throughput rate needed for many real-world applications, has also been developed. System architecture of the optical processor and the holographic memory will be presented. Application examples of this objectrecognition technology will also be demonstrated.

Acute stress induces short-term objectrecognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced objectrecognition memory changes. Adult male Wistar rats were trained in an objectrecognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term objectrecognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in objectrecognition memory. PMID:24036398

We describe a robot vision system that achieves complex objectrecognition with two layers of behaviors, performing the tasks of planning and objectrecognition, respectively. The recognition layer is a pipeline in which successive stages take in images from a stereo head, recover relevant features, build intermediate representations, and deposit 3-D objects into a world model. Each stage is an independent process that reacts automatically to output from the previous stage. This reactive system operates continuously and autonomously to construct the robot's 3-D model of the environment. Sitting above the recognition pipeline is the planner which is responsible for populating the world model with objects that satisfy the high-level goals of the system. For example, upon examination of the world model, the planner can decide to direct the head to another location, gating new images into the recognition pipeline, causing new objects to be deposited into the world model. Alternatively, the planner can alter the recognition behavior of the pipeline so that objects of a certain type or at a certain location appear in the world model.

A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for objectrecognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the objectrecognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired objectrecognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.

The benefits of integrating attention and objectrecognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and objectrecognition tasks is compared to those of models from the biological and

The benefits of integrating attention and objectrecognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and objectrecognition tasks is compared to those of models from the biological and

Traditional edge detection systems function by returning every edge in an input image. This can result in a large amount of clutter and make certain vectorization algorithms less accurate. Accuracy problems can then have a large impact on automated objectrecognitionsystems that depend on edge information. A new method of directed edge detection can be used to limit the number of edges returned based on a particular feature. This results in a cleaner image that is easier for vectorization. Vectorized edges from this process could then feed an objectrecognitionsystem where the edge data would also contain information as to what type of feature it bordered.

Objectrecognition is a typical task of aerial reconnaissance and especially in military applications, to determine the class of an unknown object on the battlefield can give valuable information on its capabilities and its threat. RecceMan® (Reconnaissance Manual) is a decision support system for objectrecognition developed by the Fraunhofer IOSB. It supports objectrecognition by automating the tedious task of matching the object features with the set of possible object classes, while leaving the assessment of features to the trained human interpreter. The quality of the features assessed by the user is influenced by several factors such as the quality of the image of the object. These factors are potential sources of error, which can lead to an incorrect classification and therefore have to be considered by the system. To address this issue, two methods for consideration of uncertainty in human feature assessment - a probabilistic and a heuristic approach - are presented and compared based on an experiment in the exemplary domain of flower recognition.

This paper presents as new approach to image recognition based on a general attraction principle. A cognitive recognition is governed by a 'focus on attention' process that concentrates on the visual data subset of task- relevant type only. Our model-based approach combines it with another process, focus on attraction, which concentrates on the transformations of visual data having relevance for the matching. The recognition process is characterized by an intentional evolution of the visual data. This chain of image transformations is viewed as driven by an attraction field that attempts to reduce the distance between the image-point and the model-point in the feature space. The field sources are determined during a learning phase, by supplying the system with a training set. The paper describes a medical interpretation case in the feature space, concerning human skin lesions. The samples of the training set, supplied by the dermatologists, allow the system to learn models of lesions in terms of features such as hue factor, asymmetry factor, and asperity factor. The comparison of the visual data with the model derives the trend of image transformations, allowing a better definition of the given image and its classification. The algorithms are implemented in C language on a PC equipped with Matrox Image Series IM-1280 acquisition and processing boards. The work is now in progress.

Understanding how the brain learns to recognize objects is one of the ultimate goals in the cognitive sciences. To date, however, we have not yet characterized the environmental factors that cause objectrecognition to emerge in the newborn brain. Here, I present the results of a high-throughput controlled-rearing experiment that examined whether the development of objectrecognition requires experience with temporally smooth visual objects. When newborn chicks (Gallus gallus) were raised with virtual objects that moved smoothly over time, the chicks developed accurate color recognition, shape recognition, and color-shape binding abilities. In contrast, when newborn chicks were raised with virtual objects that moved non-smoothly over time, the chicks' objectrecognition abilities were severely impaired. These results provide evidence for a "smoothness constraint" on newborn objectrecognition. Experience with temporally smooth objects facilitates the development of objectrecognition. PMID:27208825

Background Instantaneous object discrimination and categorization are fundamental cognitive capacities performed with the guidance of visual attention. Visual attention enables selection of a salient object within a limited area of the visual field; we referred to as “field of attention” (FA). Though there is some evidence concerning the spatial extent of objectrecognition, the following questions still remain unknown: (a) how large is the FA for rapid object categorization, (b) how accuracy of attention is distributed over the FA, and (c) how fast complex objects can be categorized when presented against backgrounds formed by natural scenes. Methodology/Principal Findings To answer these questions, we used a visual perceptual task in which subjects were asked to focus their attention on a point while being required to categorize briefly flashed (20 ms) photographs of natural scenes by indicating whether or not these contained an animal. By measuring the accuracy of categorization at different eccentricities from the fixation point, we were able to determine the spatial extent and the distribution of accuracy over the FA, as well as the speed of categorizing objects using stimulus onset asynchrony (SOA). Our results revealed that subjects are able to rapidly categorize complex natural images within about 0.1 s without eye movement, and showed that the FA for instantaneous image categorization covers a visual field extending 20°×24°, and accuracy was highest (>90%) at the center of FA and declined with increasing eccentricity. Conclusions/Significance In conclusion, human beings are able to categorize complex natural images at a glance over a large extent of the visual field without eye movement. PMID:21283690

This paper presents a detailed description and a comparative analysis of the algorithms used to determine the position and orientation of an object in real-time. The exemplary object, a freely moving gold-fish in an aquarium, provides "real-world" motion, with definable characteristics of motion (the fish never swims upside-down) and the complexities of a non-rigid body. For simplicity of implementation, and since a restricted and stationary viewing domain exists (fish-tank), we reduced the problem of obtaining 3D correspondence information to trivial alignment calculations by using two cameras orthogonally viewing the object. We applied symbolic processing techniques to recognize the 3-D orientation of a moving object of known identity in real-time. Assuming motion, each new frame (sensed by the two cameras) provides images of the object's profile which has most likely undergone translation, rotation, scaling and/or bending of the non-rigid object since the previous frame. We developed an expert system which uses heuristics of the object's motion behavior in the form of rules and information obtained via low-level image processing (like numerical inertial axis calculations) to dynamically estimate the object's orientation. An inference engine provides these estimates at frame rates of up to 10 per second (which is essentially real-time). The advantages of the rule-based approach to orientation recognition will be compared other pattern recognition techniques. Our results of an investigation of statistical pattern recognition, neural networks, and procedural techniques for orientation recognition will be included. We implemented the algorithms in a rapid-prototyping environment, the TI-Ezplorer, equipped with an Odyssey and custom imaging hardware. A brief overview of the workstation is included to clarify one motivation for our choice of algorithms. These algorithms exploit two facets of the prototype image processing and understanding workstation - both low

A control strategy for 2-D objectrecognition has been implemented on a hardware configuration which includes a Symbolics Lisp Machine (TM) as a front-end processor to a 16,384 processor Connection Machine (TM). The goal of this ongoing research program is to develop an image analysis system as an aid to human image interpretation experts. Our efforts have concentrated on 2-D objectrecognition in aerial imagery specifically, the detection and identification of aircraft near the Danbury, CT airport. Image processing functions to label and extract image features are implemented on the Connection Machine for robust computation. A model matching function was also designed and implemented on the CM for objectrecognition. In this paper we report on the integration of these algorithms on the CM, with a hierarchical control strategy to focus and guide the objectrecognition task to particular objects and regions of interest in imagery. It will be shown that these tech-nigues may be used to manipulate imagery on the order of 2k x 2k pixels in near-real-time.

Recognizing objects from arbitrary aspects is always a highly challenging problem in computer vision, and most existing algorithms mainly focus on a specific viewpoint research. Hence, in this paper we present a novel recognizing framework based on hierarchical representation, part-based method and learning in order to recognize objects from different viewpoints. The learning evaluates the model's mistakes and feeds it back the detector to avid the same mistakes in the future. The principal idea is to extract intrinsic viewpoint invariant features from the unseen poses of object, and then to take advantage of these shared appearance features to support recognition combining with the improved multiple view model. Compared with other recognition models, the proposed approach can efficiently tackle multi-view problem and promote the recognition versatility of our system. For an quantitative valuation The novel algorithm has been tested on several benchmark datasets such as Caltech 101 and PASCAL VOC 2010. The experimental results validate that our approach can recognize objects more precisely and the performance outperforms others single view recognition methods.

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognitionsystem. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

SIMBAD is a database of astronomical objects that provides (among other things) their bibliographic references in a large number of journals. Currently, these references have to be entered manually by librarians who read each paper. To cope with the increasing number of papers, CDS develops a tool to assist the librarians in their work, taking advantage of the Dictionary of Nomenclature of Celestial Objects, which keeps track of object acronyms and of their origin. The program searches for object names directly in PDF documents by comparing the words with all the formats stored in the Dictionary of Nomenclature. It also searches for variable star names based on constellation names and for a large list of usual names such as Aldebaran or the Crab. Object names found in the documents often correspond to several astronomical objects. The system retrieves all possible matches, displays them with their object type given by SIMBAD, and lets the librarian make the final choice. The bibliographic reference can then be automatically added to the object identifiers in the database. Besides, the systematic usage of the Dictionary of Nomenclature, which is updated manually, permitted to automatically check it and to detect errors and inconsistencies. Last but not least, the program collects some additional information such as the position of the object names in the document (in the title, subtitle, abstract, table, figure caption...) and their number of occurrences. In the future, this will permit to calculate the 'weight' of an object in a reference and to provide SIMBAD users with an important new information, which will help them to find the most relevant papers in the object reference list.

The ability to autonomously sense and characterize underwater objects in situ is desirable in applications of unmanned underwater vehicles (UUVs). In this work, underwater objectrecognition was explored using a digital holographic system. Two experiments were performed in which several objects of varying size, shape, and material were submerged in a 43,000 gallon test tank. Holograms were collected from each object at multiple distances and orientations, with the imager located either outside the tank (looking through a porthole) or submerged (looking downward). The resultant imagery from these holograms was preprocessed to improve dynamic range, mitigate speckle, and segment out the image of the object. A collection of feature descriptors were then extracted from the imagery to characterize various object properties (e.g., shape, reflectivity, texture). The features extracted from images of multiple objects, collected at different imaging geometries, were then used to train statistical models for objectrecognition tasks. The resulting classification models were used to perform object classification as well as estimation of various parameters of the imaging geometry. This information can then be used to inform the design of autonomous sensing algorithms for UUVs employing holographic imagers.

Microprocessors are used to show a possible implementation of a multiprocessoi system for video scene recognition operations. The system was designed in the multiple input stream and multiple data stream (MIMD) configuration. "Autonomous cooperation" among the working processors is supervised by a global operating system, the heart of which is the scheduler. The design of the scheduler and the overall operations of the system are discussed.

Real world images often contain similar objects but with different rotations, noise, or other visual alterations. Vision systems should be able to recognize objects regardless of these visual alterations. This paper presents a novel approach for learning optimized structures of classifiers for recognizing visual objects regardless of certain types of visual alterations. The approach consists of two phases. The first phase is concerned with learning classifications of a set of standard and altered objects. The second phase is concerned with discovering an optimized structure of classifiers for recognizing objects from unseen images. This paper presents an application of this approach to a domain of 15 classes of hand gestures. The experimental results show significant improvement in the recognition rate rather than using a single classifier or multiple classifiers with thresholds.

To overcome some of the problems associated with the use of ultrasonic sensors for navigation purposes, we propose a measurement system composed of three ultrasonic sensors, one transmitting and three receiving, placed on a moving vehicle. By triangulation this tri-aural sensor is able to determine the position, both distance and bearing, of the objects in the field of view. In this paper, we derive a statistical test which combines consecutive sightings by the moving sensor, of the same object to determine whether it is an edge, a plane or a corner. This test is formulated as a sequential test which guarantees that the object will be recognized after the minimal number of measurements given predetermined error probabilities. We include experimental data showing the objectrecognition capabilities of the system.

Vocal recognition is common among songbirds, and provides an excellent model system to study the perceptual and neurobiological mechanisms for processing natural vocal communication signals. Male European starlings, a species of songbird, learn to recognize the songs of multiple conspecific males by attending to stereotyped acoustic patterns, and these learned patterns elicit selective neuronal responses in auditory forebrain neurons. The present study investigates the perceptual grouping of spectrotemporal acoustic patterns in starling song at multiple temporal scales. The results show that permutations in sequencing of submotif acoustic features have significant effects on song recognition, and that these effects are specific to songs that comprise learned motifs. The observations suggest that (1) motifs form auditory objects embedded in a hierarchy of acoustic patterns, (2) that object-based song perception emerges without explicit reinforcement, and (3) that multiple temporal scales within the acoustic pattern hierarchy convey information about the individual identity of the singer. The authors discuss the results in the context of auditory object formation and talker recognition. PMID:18681620

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual objectrecognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual objectrecognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual objectrecognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual objectrecognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual objectrecognition tasks. PMID:24808564

This research features objectrecognition that exploits the context of object-action interaction to enhance the recognition performance. Since objects have specific usages, and human actions corresponding to these usages can be associated with these objects, human actions can provide effective information for objectrecognition. When objects from different categories have similar appearances, the human action associated with each object can be very effective in resolving ambiguities related to recognizing these objects. We propose an efficient method that integrates human interaction with objects into a form of objectrecognition. We represent human actions by concatenating poselet vectors computed from key frames and learn the probabilities of objects and actions using random forest and multi-class AdaBoost algorithms. Our experimental results show that poselet representation of human actions is quite effective in integrating human action information into objectrecognition. PMID:27347977

This research features objectrecognition that exploits the context of object-action interaction to enhance the recognition performance. Since objects have specific usages, and human actions corresponding to these usages can be associated with these objects, human actions can provide effective information for objectrecognition. When objects from different categories have similar appearances, the human action associated with each object can be very effective in resolving ambiguities related to recognizing these objects. We propose an efficient method that integrates human interaction with objects into a form of objectrecognition. We represent human actions by concatenating poselet vectors computed from key frames and learn the probabilities of objects and actions using random forest and multi-class AdaBoost algorithms. Our experimental results show that poselet representation of human actions is quite effective in integrating human action information into objectrecognition. PMID:27347977

L-Kynurenine (L-KYN) is a central metabolite of tryptophan degradation through the kynurenine pathway (KP). The systemic administration of L-KYN sulfate (L-KYNs) leads to a rapid elevation of the neuroactive KP metabolite kynurenic acid (KYNA). An elevated level of KYNA may have multiple effects on the synaptic transmission, resulting in complex behavioral changes, such as hypoactivity or spatial working memory deficits. These results emerged from studies that focused on rats, after low-dose L-KYNs treatment. However, in several studies neuroprotection was achieved through the administration of high-dose L-KYNs. In the present study, our aim was to investigate whether the systemic administration of a high dose of L-KYNs (300 mg/bwkg; i.p.) would produce alterations in behavioral tasks (open field or objectrecognition) in C57Bl/6j mice. To evaluate the changes in neuronal activity after L-KYNs treatment, in a separate group of animals we estimated c-Fos expression levels in the corresponding subcortical brain areas. The L-KYNs treatment did not affect the general ambulatory activity of C57Bl/6j mice, whereas it altered their moving patterns, elevating the movement velocity and resting time. Additionally, it seemed to increase anxiety-like behavior, as peripheral zone preference of the open field arena emerged and the rearing activity was attenuated. The treatment also completely abolished the formation of objectrecognition memory and resulted in decreases in the number of c-Fos-immunopositive-cells in the dorsal part of the striatum and in the CA1 pyramidal cell layer of the hippocampus. We conclude that a single exposure to L-KYNs leads to behavioral disturbances, which might be related to the altered basal c-Fos protein expression in C57Bl/6j mice. PMID:26136670

A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving objectrecognition and localization. This paper presents two approaches for haptic objectrecognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic objectrecognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments. PMID:24553087

Tactile sensors, because of their intrinsic insensitivity to lighting conditions and water turbidity, provide promising opportunities for augmenting the capabilities of vision sensors in applications involving objectrecognition and localization. This paper presents two approaches for haptic objectrecognition and localization for ground and underwater environments. The first approach called Batch Ransac and Iterative Closest Point augmented Particle Filter (BRICPPF) is based on an innovative combination of particle filters, Iterative-Closest-Point algorithm, and a feature-based Random Sampling and Consensus (RANSAC) algorithm for database matching. It can handle a large database of 3D-objects of complex shapes and performs a complete six-degree-of-freedom localization of static objects. The algorithms are validated by experimentation in ground and underwater environments using real hardware. To our knowledge this is the first instance of haptic objectrecognition and localization in underwater environments. The second approach is biologically inspired, and provides a close integration between exploration and recognition. An edge following exploration strategy is developed that receives feedback from the current state of recognition. A recognition by parts approach is developed which uses the BRICPPF for object sub-part recognition. Object exploration is either directed to explore a part until it is successfully recognized, or is directed towards new parts to endorse the current recognition belief. This approach is validated by simulation experiments. PMID:24553087

The Defense Communications Division of ITT (ITTDCD) has developed an automatic speaker recognition (ASR) system that meets the functional requirements defined in NRL's Statement of Work. This report is organized as follows. Chapter 2 is a short history of the development of the ASR system, both the algorithm and the implementation. Chapter 3 describes the methodology of system testing, and Chapter 4 summarizes test results. In Chapter 5, some additional testing performed using GFM test material is discussed. Conclusions derived from the contract work are given in Chapter 6.

We performed an event-related potential study to investigate the self-relevance effect in objectrecognition. Three stimulus categories were prepared: SELF (participant's own objects), FAMILIAR (disposable and public objects, defined as objects with less-self-relevant familiarity), and UNFAMILIAR (others' objects). The participants' task was to…

Behavioral studies of objectrecognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of objectrecognition are best understood. Here, we review this research and suggest that a core set of mechanisms for objectrecognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of objectrecognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of objectrecognition in the primate ventral stream, plays a role in objectrecognition by pigeons. We also highlight differences between pigeons and people in objectrecognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of objectrecognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human objectrecognition from studying the “simple” brains of pigeons. PMID:25352784

The role of the semantic system in recognizing objects is a matter of debate. Connectionist theories argue that it is impossible for a participant to determine that an object is familiar to them without recourse to a semantic hub; localist theories state that accessing a stored representation of the visual features of the object is sufficient for recognition. We examine this issue through the longitudinal study of two cases of semantic dementia, a neurodegenerative disorder characterized by a progressive degradation of the semantic system. The cases in this paper do not conform to the "common" pattern of objectrecognition performance in semantic dementia described by Rogers, T. T., Lambon Ralph, M. A., Hodges, J. R., & Patterson, K. (2004). Natural selection: The impact of semantic impairment on lexical and object decision. Cognitive Neuropsychology, 21, 331-352., and show no systematic relationship between severity of semantic impairment and success in object decision. We argue that these data are inconsistent with the connectionist position but can be easily reconciled with localist theories that propose stored structural descriptions of objects outside of the semantic system. PMID:27355607

We explored infants' ability to recognize the canonical colors of daily objects, including two color-specific objects (human face and fruit) and a non-color-specific object (flower), by using a preferential looking technique. A total of 58 infants between 5 and 8 months of age were tested with a stimulus composed of two color pictures of an object…

To solve the pattern recognition problem, a method of synthesized phase objects is suggested. The essence of the suggested method is that synthesized phase objects are used instead of real amplitude objects. The former is object-dependent phase distributions calculated using the iterative Fourier-transform (IFT) algorithm. The method is experimentally studied with a Vander Lugt optical-digital 4F-correlator. We present the comparative analysis of recognition results using conventional and proposed methods, estimate the sensitivity of the latter to distortions of the structure of objects, and determine the applicability limits. It is demonstrated that the proposed method allows one: (а) to simplify the procedure of choice of recognition signs (criteria); (b) to obtain one-type δ-like recognition signals irrespective of the type of objects; (с) to improve signal-to-noise ratio (SNR) for correlation signals by 20 - 30 dB on average. The spatial separation of the Fourier-spectra of objects and optical noises of the correlator by means of the superposition of the phase grating on recognitionobjects at the recording of holographic filters and at the matched filtering has additionally improved SNR (>10 dB) for correlation signals. To introduce recognitionobjects in the correlator, we use a SLM LC-R 2500 device. Matched filters are recorded on a self-developing photopolymer. PMID:23388812

Are all categories of objects recognized in the same manner visually? Evidence from neuropsychology suggests they are not: some brain damaged patients are more impaired in recognizing natural objects than artefacts whereas others show the opposite impairment. Category-effects have also been demonstrated in neurologically intact subjects, but the…

The overall performances of objectrecognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring objectrecognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Visual and haptic unisensory object processing show many similarities in terms of categorization, recognition, and representation. In this review, we discuss how these similarities contribute to multisensory object processing. In particular, we show that similar unisensory visual and haptic representations lead to a shared multisensory representation underlying both cross-modal objectrecognition and view-independence. This shared representation suggests a common neural substrate and we review several candidate brain regions, previously thought to be specialized for aspects of visual processing, that are now known also to be involved in analogous haptic tasks. Finally, we lay out the evidence for a model of multisensory objectrecognition in which top-down and bottom-up pathways to the object-selective lateral occipital complex are modulated by object familiarity and individual differences in object and spatial imagery. PMID:25101014

In the novel objectrecognition (OR) paradigm, rats are placed in an arena where they encounter two sample objects during a familiarization phase. A few minutes later, they are returned to the same arena and are presented with a familiar object and a novel object. The object location recognition (OL) variant involves the same familiarization procedure but during testing one of the familiar objects is placed in a novel location. Normal adult rats are able to perform both the OR and OL tasks, as indicated by enhanced exploration of the novel vs. the familiar test item. Rats with hippocampal lesions perform the OR but not OL task indicating a role of spatial memory in OL. Recently, these tasks have been used to study the ontogeny of spatial memory but the literature has yielded conflicting results. The current experiments add to this literature by: (1) behaviorally characterizing these paradigms in postnatal day (PD) 21, 26 and 31-day-old rats; (2) examining the role of NMDA systems in OR vs. OL; and (3) investigating the effects of neonatal alcohol exposure on both tasks. Results indicate that normal-developing rats are able to perform OR and OL by PD21, with greater novelty exploration in the OR task at each age. Second, memory acquisition in the OL but not OR task requires NMDA receptor function in juvenile rats [corrected]. Lastly, neonatal alcohol exposure does not disrupt performance in either task. Implications for the ontogeny of incidental spatial learning and its disruption by developmental alcohol exposure are discussed. PMID:23933466

Perceptual decisions seem to be made automatically and almost instantly. Constructing a unitary subjective conscious experience takes more time. For example, when trying to avoid a collision with a car on a foggy road you brake or steer away in a reflex, before realizing you were in a near accident. This subjective aspect of objectrecognition has been given little attention. We used metacognition (assessed with confidence ratings) to measure subjective experience during object detection and object categorization for degraded and masked objects, while objective performance was matched. Metacognition was equal for degraded and masked objects, but categorization led to higher metacognition than did detection. This effect turned out to be driven by a difference in metacognition for correct rejection trials, which seemed to be caused by an asymmetry of the distractor stimulus: It does not contain object-related information in the detection task, whereas it does contain such information in the categorization task. Strikingly, this asymmetry selectively impacted metacognitive ability when objective performance was matched. This finding reveals a fundamental difference in how humans reflect versus act on information: When matching the amount of information required to perform two tasks at some objective level of accuracy (acting), metacognitive ability (reflecting) is still better in tasks that rely on positive evidence (categorization) than in tasks that rely more strongly on an absence of evidence (detection). PMID:24554231

We present a technique called Random Image Structure Evolution (RISE) for use in experimental investigations of high-level visual perception. Potential applications of RISE include the quantitative measurement of perceptual hysteresis and priming, the study of the neural substrates of object perception, and the assessment and detection of subtle…

In the visual perception literature, the recognition of faces has often been contrasted with that of non-face objects, in terms of differences with regard to the role of parts, part relations and holistic processing. However, recent evidence from developmental studies has begun to blur this sharp distinction. We review evidence for a protracted development of objectrecognition that is reminiscent of the well-documented slow maturation observed for faces. The prolonged development manifests itself in a retarded processing of metric part relations as opposed to that of individual parts and offers surprising parallels to developmental accounts of face recognition, even though the interpretation of the data is less clear with regard to holistic processing. We conclude that such results might indicate functional commonalities between the mechanisms underlying the recognition of faces and non-face objects, which are modulated by different task requirements in the two stimulus domains. PMID:27014176

A sensing system locates an object by sensing the object's effect on electric fields. The object's effect on the mutual capacitance of electrode pairs varies according to the distance between the object and the electrodes. A single electrode pair can sense the distance from the object to the electrodes. Multiple electrode pairs can more precisely locate the object in one or more dimensions.

A sensing system locates an object by sensing the object`s effect on electric fields. The object`s effect on the mutual capacitance of electrode pairs varies according to the distance between the object and the electrodes. A single electrode pair can sense the distance from the object to the electrodes. Multiple electrode pairs can more precisely locate the object in one or more dimensions. 12 figs.

Based on the geon structural description approach, I. Biederman and P.C. Gerhardstein (1993) proposed 3 conditions under which objectrecognition is predicted to be viewpoint invariant. Two experiments are reported that satisfied all 3 criteria yet revealed performance that was clearly viewpoint dependent. Experiment 1 demonstrated that for both sequential matching and naming tasks, recognition of qualitatively distinct objects became progressively longer and less accurate as the viewpoint difference between study and test viewpoints increased. Experiment 2 demonstrated that for single-part objects, larger effects of viewpoint occurred when there was a change in the visible structure, indicating sensitivity to qualitative features in the image, not geon structural descriptions. These results suggest that the conditions proposed by I. Biederman and P.C. Gerhardstein are not generally applicable, the recognition of qualitatively distinct objects often relies on viewpoint-dependent mechanisms, and the molar features of view-based mechanisms appear to be image features rather than geons. PMID:9411023

In this functional MRI experiment, encoding of objects was associated with activation in left ventrolateral prefrontal/insular and right dorsolateral prefrontal and fusiform regions as well as in the left putamen. By contrast, correct recognition of previously learned objects (R judgments) produced activation in left superior frontal, bilateral…

Previous work on the effect of aging on spontaneous objectrecognition (SOR) memory tasks in rats has yielded controversial results. Although the results at long-retention intervals are consistent, conflicting results have been reported at shorter delays. We have assessed the potential relevance of the type of object used in the performance of…

Williams syndrome (WS) is a rare genetic disorder that results in severe visual-spatial cognitive deficits coupled with relative sparing in language, face recognition, and certain aspects of motion processing. Here, we look for evidence for sparing or impairment in another cognitive system--objectrecognition. Children with WS, normal mental-age…

Recognizing objects is difficult because it requires both linking views of an object that can be different and distinguishing objects with similar appearance. Interestingly, people can learn to recognize objects across views in an unsupervised way, without feedback, just from the natural viewing statistics. However, there is intense debate regarding what information during unsupervised learning is used to link among object views. Specifically, researchers argue whether temporal proximity, motion, or spatiotemporal continuity among object views during unsupervised learning is beneficial. Here, we untangled the role of each of these factors in unsupervised learning of novel three-dimensional (3-D) objects. We found that after unsupervised training with 24 object views spanning a 180° view space, participants showed significant improvement in their ability to recognize 3-D objects across rotation. Surprisingly, there was no advantage to unsupervised learning with spatiotemporal continuity or motion information than training with temporal proximity. However, we discovered that when participants were trained with just a third of the views spanning the same view space, unsupervised learning via spatiotemporal continuity yielded significantly better recognition performance on novel views than learning via temporal proximity. These results suggest that while it is possible to obtain view-invariant recognition just from observing many views of an object presented in temporal proximity, spatiotemporal information enhances performance by producing representations with broader view tuning than learning via temporal association. Our findings have important implications for theories of objectrecognition and for the development of computational algorithms that learn from examples. PMID:26024454

Automatic objectrecognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in objectrecognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid objectrecognition. Here, we report results of an MSC algorithm applied to objectrecognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

This paper presents an experimental study of the implementation of a face recognitionsystem in embedded systems. To investigate the feasibility and practicality of real time face recognition on such systems, a door access control system based on face recognition is built. Due to the limited computation power of embedded device, a semi-automatic scheme for face detection and eye location is proposed to solve these computationally hard problems. It is found that to achieve real time performance, optimization of the core face recognition module is needed. As a result, extensive profiling is done to pinpoint the execution hotspots in the system and optimization are carried out. After careful precision analysis, all slow floating point calculations are replaced with their fixed-point versions. Experimental results show that real time performance can be achieved without significant loss in recognition accuracy.

The Extravehicular Activity Helper/Retriever (EVAHR) is a robotic device currently under development at the NASA Johnson Space Center that is designed to fetch objects or to assist in retrieving an astronaut who may have become inadvertently de-tethered. The EVAHR will be required to exhibit a high degree of intelligent autonomous operation and will base much of its reasoning upon information obtained from one or more three-dimensional sensors that it will carry and control. At the highest level of visual cognition and reasoning, the EVAHR will be required to detect objects, recognize them, and estimate their spatial orientation and location. The recognition phase and estimation of spatial pose will depend on the ability of the vision system to reliably extract geometric features of the objects such as whether the surface topologies observed are planar or curved and the spatial relationships between the component surfaces. In order to achieve these tasks, three-dimensional sensing of the operational environment and objects in the environment will therefore be essential. One of the sensors being considered to provide image data for objectrecognition and pose estimation is a phase-shift laser scanner. The characteristics of the data provided by this scanner have been studied and algorithms have been developed for segmenting range images into planar surfaces, extracting basic features such as surface area, and recognizing the object based on the characteristics of extracted features. Also, an approach has been developed for estimating the spatial orientation and location of the recognized object based on orientations of extracted planes and their intersection points. This paper presents some of the algorithms that have been developed for the purpose of recognizing and estimating the pose of objects as viewed by the laser scanner, and characterizes the desirability and utility of these algorithms within the context of the scanner itself, considering data quality and

The objectives of the pattern recognition tasks are to develop (1) a man-machine interactive data processing system; and (2) procedures to determine effective features as a function of time for crops and soils. The signal analysis and dissemination equipment, SADE, is being developed as a man-machine interactive data processing system. SADE will provide imagery and multi-channel analog tape inputs for digitation and a color display of the data. SADE is an essential tool to aid in the investigation to determine useful features as a function of time for crops and soils. Four related studies are: (1) reliability of the multivariate Gaussian assumption; (2) usefulness of transforming features with regard to the classifier probability of error; (3) advantage of selecting quantizer parameters to minimize the classifier probability of error; and (4) advantage of using contextual data. The study of transformation of variables (features), especially those experimental studies which can be completed with the SADE system, will be done.

Atmospheric scattering causes significant degradation in the quality of video images, particularly when imaging over long distances. The principle problem is the reduction in contrast due to scattered light. It is known that when the scattering particles are not too large compared with the imaging wavelength (i.e. Mie scattering) then high spatial resolution information may be contained within a low-contrast image. Unfortunately this information is not easily perceived by a human observer, particularly when using a standard video monitor. A secondary problem is the difficulty of achieving a sharp focus since automatic focus techniques tend to fail in such conditions. Recently several commercial colour video processing systems have become available. These systems use various techniques to improve image quality in low contrast conditions whilst retaining colour content. These systems produce improvements in subjective image quality in some situations, particularly in conditions of haze and light fog. There is also some evidence that video enhancement leads to improved ATR performance when used as a pre-processing stage. Psychological literature indicates that low contrast levels generally lead to a reduction in the performance of human observers in carrying out simple visual tasks. The aim of this paper is to present the results of an empirical study on objectrecognition in adverse viewing conditions. The chosen visual task was vehicle number plate recognition at long ranges (500 m and beyond). Two different commercial video enhancement systems are evaluated using the same protocol. The results show an increase in effective range with some differences between the different enhancement systems.

An object in an image when analyzed further will show the characteristics that distinguish one object with another object in an image. Characteristics that are used in objectrecognition in an image can be a color, shape, pattern, texture and spatial information that can be used to represent objects in the digital image. The method has recently been developed for image feature extraction on objects that share characteristics curve analysis (simple curve) and use the search feature of chain code object. This study will develop an algorithm analysis and the recognition of the type of curve as the basis for objectrecognition in images, with proposing addition of complex curve characteristics with maximum four branches that will be used for the process of objectrecognition in images. Definition of complex curve is the curve that has a point of intersection. By using some of the image of the edge detection, the algorithm was able to do the analysis and recognition of complex curve shape well.

In order to strengthen the capability of the space debris researches, automated optical observation becomes more and more popular. Thus, the fully unattended automated objectrecognition framework is urgently needed to be studied. On the other hand, the open loop tracking which guides the telescope only with historical orbital elements is a simple and robust way to track space debris. According to the analysis of point distribution characteristics in pixel domain of object's open loop tracking image sequence, the Cluster Identification Method is introduced into automated space debris recognition method. With the comparison of three algorithm implements, it is shown that this method is totally available in actual research work.

Mechanisms of explicit objectrecognition are often difficult to investigate and require stimuli with controlled features whose expression can be manipulated in a precise quantitative fashion. Here, we developed a novel method (called “Dots”), for generating visual stimuli, which is based on the progressive deformation of a regular lattice of dots, driven by local contour information from images of objects. By applying progressively larger deformation to the lattice, the latter conveys progressively more information about the target object. Stimuli generated with the presented method enable a precise control of object-related information content while preserving low-level image statistics, globally, and affecting them only little, locally. We show that such stimuli are useful for investigating objectrecognition under a naturalistic setting – free visual exploration – enabling a clear dissociation between object detection and explicit recognition. Using the introduced stimuli, we show that top-down modulation induced by previous exposure to target objects can greatly influence perceptual decisions, lowering perceptual thresholds not only for objectrecognition but also for object detection (visual hysteresis). Visual hysteresis is target-specific, its expression and magnitude depending on the identity of individual objects. Relying on the particular features of dot stimuli and on eye-tracking measurements, we further demonstrate that top-down processes guide visual exploration, controlling how visual information is integrated by successive fixations. Prior knowledge about objects can guide saccades/fixations to sample locations that are supposed to be highly informative, even when the actual information is missing from those locations in the stimulus. The duration of individual fixations is modulated by the novelty and difficulty of the stimulus, likely reflecting cognitive demand. PMID:21818397

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant objectrecognition domain and 98 percent recognition accuracy in the 3D invariant objectrecognition domain after training on only a small sample of transformed views of the objects.

We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based objectrecognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D objectrecognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the Expectation-Maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood

Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic objectrecognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of objectrecognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically plausible computational models of (bottom-up) pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task. PMID:22110461

Recognition of 3D objects independent of size, position, and rotation is an important and difficult subject in computer vision. A 3D feature extraction method referred to as the Open Ball Operator (OBO) is proposed as an approach to solving the 3D objectrecognition problem. The OBO feature extraction method has the three characteristics of invariance to rotation, scaling, and translation invariance. Additionally, the OBO is capable of distinguishing between convexities and concavities in the surface of 3D object. The OBO also exhibits a good robustness to noise and uncertainty caused by inaccuracies in 3D measurements. A wavelet de- noising method is used for filtering out noise contained in the feature vectors of 3D objects.

Background: Previous studies have demonstrated that methamphetamine abuse leads to memory deficits and these are associated with relapse. Furthermore, extensive evidence indicates that nicotine prevents and/or improves memory deficits in different models of cognitive dysfunction and these nicotinic effects might be mediated by hippocampal or cortical nicotinic acetylcholine receptors. The present study investigated whether nicotine attenuates methamphetamine-induced novel objectrecognition deficits in rats and explored potential underlying mechanisms. Methods: Adolescent or adult male Sprague-Dawley rats received either nicotine water (10–75 μg/mL) or tap water for several weeks. Methamphetamine (4×7.5mg/kg/injection) or saline was administered either before or after chronic nicotine exposure. Novel objectrecognition was evaluated 6 days after methamphetamine or saline. Serotonin transporter function and density and α4β2 nicotinic acetylcholine receptor density were assessed on the following day. Results: Chronic nicotine intake via drinking water beginning during either adolescence or adulthood attenuated the novel objectrecognition deficits caused by a high-dose methamphetamine administration. Similarly, nicotine attenuated methamphetamine-induced deficits in novel objectrecognition when administered after methamphetamine treatment. However, nicotine did not attenuate the serotonergic deficits caused by methamphetamine in adults. Conversely, nicotine attenuated methamphetamine-induced deficits in α4β2 nicotinic acetylcholine receptor density in the hippocampal CA1 region. Furthermore, nicotine increased α4β2 nicotinic acetylcholine receptor density in the hippocampal CA3, dentate gyrus and perirhinal cortex in both saline- and methamphetamine-treated rats. Conclusions: Overall, these findings suggest that nicotine-induced increases in α4β2 nicotinic acetylcholine receptors in the hippocampus and perirhinal cortex might be one mechanism by which

Adult rats with extensive, bilateral neurotoxic lesions of the hippocampus showed normal forgetting curves for objectrecognition memory, yet were impaired on closely related tests of object recency memory. The present findings point to specific mechanisms for temporal order information (recency) that are dependent on the hippocampus and do not involve objectrecognition memory. The objectrecognition tests measured rats exploring simultaneously presented objects, one novel and the other familiar. Task difficulty was varied by altering the retention delays after presentation of the familiar object, so creating a forgetting curve. Hippocampal lesions had no apparent effect, despite using an apparatus (bow-tie maze) where it was possible to give lists of objects that might be expected to increase stimulus interference. In contrast, the same hippocampal lesions impaired the normal preference for an older (less recent) familiar object over a more recent, familiar object. A correlation was found between the loss of septal hippocampal tissue and this impairment in recency memory. The dissociation in the present study between recognition memory (spared) and recency memory (impaired) was unusually compelling, because it was possible to test the same objects for both forms of memory within the same session and within the same apparatus. The object recency deficit is of additional interest as it provides an example of a nonspatial memory deficit following hippocampal damage. PMID:23025831

Invariant visual objectrecognition is the ability to recognize visual objects despite the vastly different images that each object can project onto the retina during natural vision, depending on its position and size within the visual field, its orientation relative to the viewer, etc. Achieving invariant recognition represents such a formidable computational challenge that is often assumed to be a unique hallmark of primate vision. Historically, this has limited the invasive investigation of its neuronal underpinnings to monkey studies, in spite of the narrow range of experimental approaches that these animal models allow. Meanwhile, rodents have been largely neglected as models of object vision, because of the widespread belief that they are incapable of advanced visual processing. However, the powerful array of experimental tools that have been developed to dissect neuronal circuits in rodents has made these species very attractive to vision scientists too, promoting a new tide of studies that have started to systematically explore visual functions in rats and mice. Rats, in particular, have been the subjects of several behavioral studies, aimed at assessing how advanced objectrecognition and shape processing is in this species. Here, I review these recent investigations, as well as earlier studies of rat pattern vision, to provide an historical overview and a critical summary of the status of the knowledge about rat object vision. The picture emerging from this survey is very encouraging with regard to the possibility of using rats as complementary models to monkeys in the study of higher-level vision. PMID:25561421

Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

Moving dots can evoke a percept of the spatial structure of a three-dimensional object in the absence of other visual cues. This phenomenon, called structure from motion (SFM), suggests that the motion flowfield represented in the dorsal stream can form the basis of objectrecognition performed in the ventral stream. SFM processing is likely to contribute to object perception whenever there is relative motion between the observer and the object viewed. Here we investigate the motion flowfield component of objectrecognition with functional magnetic resonance imaging. Our SFM stimuli encoded face surfaces and random three-dimensional control shapes with matched curvature properties. We used two different types of an SFM stimulus with the dots either fixed to the surface of the object or moving on it. Despite the radically different encoding of surface structure in the two types of SFM, both elicited strong surface percepts and involved the same network of cortical regions. From early visual areas, this network extends dorsally into the human motion complex and parietal regions and ventrally into object-related cortex. The SFM stimuli elicited a face-selective response in the fusiform face area. The human motion complex appears to have a central role in SFM objectrecognition, not merely representing the motion flowfield but also the surface structure of the motion-defined object. The motion complex and a region in the intraparietal sulcus reflected the motion state of the SFM-implicit object, responding more strongly when the implicit object was in motion than when it was stationary. PMID:12598634

This study presents an applicability of real-time microradiography to biological objects, namely to horse chestnut leafminer, Cameraria ohridella (Insecta: Lepidoptera, Gracillariidae) and following image processing focusing on image segmentation and objectrecognition. The microradiography of insects (such as horse chestnut leafminer) provides a non-invasive imaging that leaves the organisms alive. The imaging requires a high spatial resolution (micrometer scale) radiographic system. Our radiographic system consists of a micro-focus X-ray tube and two types of detectors. The first is a charge integrating detector (Hamamatsu flat panel), the second is a pixel semiconductor detector (Medipix2 detector). The latter allows detection of single quantum photon of ionizing radiation. We obtained numerous horse chestnuts leafminer pupae in several microradiography images easy recognizable in automatic mode using the image processing methods. We implemented an algorithm that is able to count a number of dead and alive pupae in images. The algorithm was based on two methods: 1) noise reduction using mathematical morphology filters, 2) Canny edge detection. The accuracy of the algorithm is higher for the Medipix2 (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.83), than for the flat panel (average recall for detection of alive pupae =0.99, average recall for detection of dead pupae =0.77). Therefore, we conclude that Medipix2 has lower noise and better displays contours (edges) of biological objects. Our method allows automatic selection and calculation of dead and alive chestnut leafminer pupae. It leads to faster monitoring of the population of one of the world's important insect pest.

The recognitionsystem rapid application prototyping tool (RSRAPT) was developed to evaluate various potential configurations of miniature ruggedized optical correlator (MROC) modules and to rapidly assess the feasibility of their use within systems such as missile seekers. RSRAPT is a simulation environment for rapidly prototyping, developing, and evaluating recognitionsystems that incorporate MROC technology. It is designed to interface to OLE compliant Windows applications using standard OLE interfaces. The system consists of nine key functional elements: sensor, detection, segmentation, pre-processor, filter selection, correlator, post-processor, identifier, and controller. The RSRAPT is a collection of object oriented server components, a client user interface and a recognitionssystem image and image sensor database. The server components are implemented to encapsulate processes that are typical to any optical-correlator based pattern recognitionsystem. All the servers are implemented as Microsoft component object model objects. In addition to the system servers there are two key 'helper servers.' The first is the image server, which encapsulates all 'images'. This includes gray scale images and even complex images. The other supporting server is the filter generation server. This server trains the system on user data by calculating filters for user selected image types. The system hosts a library of standard image processing routines such as convolution, edge operators, clustering algorithms, median filtering, morphological operators such as erosion and dilation, connected components, region growing, and adaptive thresholding. In this paper we describe the simulator and show sample results from diverse applications.

We investigated the importance and efficiency of active and passive exploration on the recognition of objects in a variety of virtual environments (VEs). In this study, 54 participants were randomly allocated into one of active and passive navigation conditions. Active navigation was performed by allowing participants to self-pace and control their own navigation, but passive navigation was conducted by forced navigation. After navigating VEs, participants were asked to recognize the objects that had been in the VEs. Active navigation condition had a significantly higher percentage of hit responses (t (52) = 4.000, p < 0.01), and a significantly lower percentage of miss responses (t (52) = -3.763, p < 0.01) in objectrecognition than the passive condition. These results suggest that active navigation plays an important role in spatial cognition as well as providing an explanation for the efficiency of learning in a 3D-based program. PMID:17474852

This paper considers the design of an object cueing system as a rule-based expert system. The architecture is modular and the control strategy permits dynamic scheduling of tasks. In this approach, results of several algorithms and many objectrecognition heuristics are combined to achieve better performance levels. Importance of spatial knowledge representatiOn is also discussed.

This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the objectrecognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts: panoramic image acquisition, multiple objectrecognition, and grid-based localization. Multiple objectrecognition information from panoramic images is utilized in the localization part. High-level object information was useful not only for global localization, but also for robot-object interactions. The metric global localization (position, viewing direction) was conducted based on the bearing information of recognized objects from just one panoramic image. The feasibility of the novel localization paradigm was validated experimentally. PMID:26457323

The neural mechanisms subserving visual recognition are traditionally described in terms of bottom-up analysis, whereby increasingly complex aspects of the visual input are processed along a hierarchical progression of cortical regions. However, the importance of top-down facilitation in successful recognition has been emphasized in recent models and research findings. Here we consider evidence for top-down facilitation of recognition that is triggered by early information about an object, as well as by contextual associations between an object and other objects with which it typically appears. The object-based mechanism is proposed to trigger top-down facilitation of visual recognition rapidly, using a partially analyzed version of the input image (i.e., a blurred image) that is projected from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC information that is back-projected as "initial guesses" to the temporal cortex where it presensitizes the most likely interpretations of the input object. In addition to this object-based facilitation, a context-based mechanism is proposed to trigger top-down facilitation through contextual associations between objects in scenes. These contextual associations activate predictive information about which objects are likely to appear together, and can influence the "initial guesses" about an object's identity. We have shown that contextual associations are analyzed by a network that includes the parahippocampal cortex and the retrosplenial complex. The integrated proposal described here is that object- and context-based top-down influences operate together, promoting efficient recognition by framing early information about an object within the constraints provided by a lifetime of experience with contextual associations. PMID:17027376

Research into the neural basis of recognition memory has traditionally focused on the remembrance of visual stimuli. The present study examined the neural basis of objectrecognition memory in the dark, with a view to determining the extent to which it shares common pathways with visual-based objectrecognition. Experiment 1 assessed the expression of the immediate-early gene c-fos in rats that discriminated novel from familiar objects in the dark (Group Novel). Comparisons made with a control group that explored only familiar objects (Group Familiar) showed that Group Novel had higher c-fos activity in the rostral perirhinal cortex and the lateral entorhinal cortex. Outside the temporal region, Group Novel showed relatively increased c-fos activity in the anterior medial thalamic nucleus and the anterior cingulate cortex. Both the hippocampal CA fields and the granular retrosplenial cortex showed borderline increases in c-fos activity with object novelty. The hippocampal findings prompted Experiment 2. Here, rats with hippocampal lesions were tested in the dark for objectrecognition memory at different retention delays. Across two replications, no evidence was found that hippocampal lesions impair nonvisual objectrecognition. The results indicate that in the dark, as in the light, interrelated parahippocampal sites are activated when rats explore novel stimuli. These findings reveal a network of linked c-fos activations that share superficial features with those associated with visual recognition but differ in the fine details; for example, in the locus of the perirhinal cortex activation. While there may also be a relative increase in c-fos activation in the extended-hippocampal system to objectrecognition in the dark, there was no evidence that this recognition memory problem required an intact hippocampus. PMID:23244291

Standard models of the visual objectrecognition pathway hold that a largely feedforward process from the retina through inferotemporal cortex leads to object identification. A subsequent feedback process originating in frontoparietal areas through reciprocal connections to striate cortex provides attentional support to salient or behaviorally-relevant features. Here, we review mounting evidence that feedback signals also originate within extrastriate regions and begin during the initial feedforward process. This feedback process is temporally dissociable from attention and provides important functions such as grouping, associational reinforcement, and filling-in of features. Local feedback signals operating concurrently with feedforward processing are important for object identification in noisy real-world situations, particularly when objects are partially occluded, unclear, or otherwise ambiguous. Altogether, the dissociation of early and late feedback processes presented here expands on current models of object identification, and suggests a dual role for descending feedback projections. PMID:25071647

Visual objectrecognition is of fundamental importance in our everyday interaction with the environment. Recent models of visual perception emphasize the role of top-down predictions facilitating objectrecognition via initial guesses that limit the number of object representations that need to be considered. Several results suggest that this rapid and efficient object processing relies on the early extraction and processing of low spatial frequencies (LSF). The present study aimed to investigate the SF content of visual object representations and its modulation by contextual and affective values of the perceived object during a picture-name verification task. Stimuli consisted of pictures of objects equalized in SF content and categorized as having low or high affective and contextual values. To access the SF content of stored visual representations of objects, SFs of each image were then randomly sampled on a trial-by-trial basis. Results reveal that intermediate SFs between 14 and 24 cycles per object (2.3–4 cycles per degree) are correlated with fast and accurate identification for all categories of objects. Moreover, there was a significant interaction between affective and contextual values over the SFs correlating with fast recognition. These results suggest that affective and contextual values of a visual object modulate the SF content of its internal representation, thus highlighting the flexibility of the visual recognitionsystem. PMID:24904514

A portable system is provided that is operational for determining, with three dimensional resolution, the position of a buried object or approximately positioned object that may move in space or air or gas. The system has a plurality of receivers for detecting the signal front a target antenna and measuring the phase thereof with respect to a reference signal. The relative permittivity and conductivity of the medium in which the object is located is used along with the measured phase signal to determine a distance between the object and each of the plurality of receivers. Knowing these distances. an iteration technique is provided for solving equations simultaneously to provide position coordinates. The system may also be used for tracking movement of an object within close range of the system by sampling and recording subsequent position of the object. A dipole target antenna. when positioned adjacent to a buried object, may be energized using a separate transmitter which couples energy to the target antenna through the medium. The target antenna then preferably resonates at a different frequency, such as a second harmonic of the transmitter frequency.

Mounting evidence suggests that “core objectrecognition,” the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains little-understood. Here we review evidence ranging from individual neurons, to neuronal populations, to behavior, to computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical sub-networks with a common functional goal. PMID:22325196

This study extended aspects of Biederman's (1987) recognition-by-components (RBC) theory to the analysis of age differences in the recognition of incomplete visually-presented objects. RBC theory predicts that objects are recognizable or recoverable under conditions of fragmentation if a sufficient amount of essential structural information remains available. Objects are rendered nonrecoverable by the omission or obstruction of essential structural features at vertices and areas of concavity. Fifteen young adults and 15 older adults participated in a study of the effects of amount (25%, 45%, 65%) and type of fragmentation (recoverable, nonrecoverable) on object naming. Age-related declines in recognizing incomplete objects were associated with the amount of fragmentation, but type of fragmentation did not affect the performance of older adults. For the young adults, accuracy of performance was affected by both amount and type of fragmentation, consistent with Biederman's RBC theory. These results were interpreted as suggesting that age-related declines in perceptual closure performance have to do with non-structural factors such as the ability to inferentially augment degraded or missing visual information. PMID:1446700

“Invariant object recognition” refers to the ability to recognize objects across variation in their appearance on the retina. This ability is central to visual perception, yet its developmental origins are poorly understood. Traditionally, nonhuman primates, rats, and pigeons have been the most commonly used animal models for studying invariant objectrecognition. Although these animals have many advantages as model systems, they are not well suited for studying the emergence of invariant objectrecognition in the newborn brain. Here, we argue that newly hatched chicks (Gallus gallus) are an ideal model system for studying the emergence of invariant objectrecognition. Using an automated controlled-rearing approach, we show that chicks can build a viewpoint-invariant representation of the first object they see in their life. This invariant representation can be built from highly impoverished visual input (three images of an object separated by 15° azimuth rotations) and cannot be accounted for by low-level retina-like or V1-like neuronal representations. These results indicate that newborn neural circuits begin building invariant object representations at the onset of vision and argue for an increased focus on chicks as an animal model for studying invariant objectrecognition. PMID:25767436

Novices recognize objects at the basic-category level (e.g., dog, chair, and bird) at which identification is based on the global form of the objects (Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). In contrast, experts recognize objects within their domain of expertise at the subordinate level (e.g., Sparrow or Finch) for which the internal object information may play an important role in identification (Tanaka & Taylor, 1991). To investigate whether expert recognition relies on internal object information, we band-pass filtered bird images over a range of spatial frequencies (SF) and then masked the filtered image to preserve its global form. In Experiment 1, bird experts categorized common birds at the family level (e.g., Robin or Sparrow) more quickly and more accurately than novices. Both experts and novices were more accurate when bird images contained the internal information represented by a middle range of SFs, and this finding was characterized by a quadratic function in which accuracy decreased toward each end of the SF spectrum. However, the experts, but not the novices, showed a similar quadratic relationship between response times and SF range. In Experiment 2, experts categorized Warblers and Finches at the more specific, species level (e.g., Wilson's Warbler or House Finch). Recognition was again fastest and most accurate for images filtered in the middle range of SFs. Collectively, these results indicate that a midrange of SFs contain crucial information for subordinate recognition, and that extensive perceptual experience can influence the efficiency with which this information is utilized. (PsycINFO Database Record PMID:26480250

Objects contain rich visual and conceptual information, but do these two types of information interact? Here, we examine whether visual and conceptual information interact when observers see novel objects for the first time. We then address how this interaction influences the acquisition of perceptual expertise. We used two types of novel objects (Greebles), designed to resemble either animals or tools, and two lists of words, which described non-visual attributes of people or man-made objects. Participants first judged if a word was more suitable for describing people or objects while ignoring a task-irrelevant image, and showed faster responses if the words and the unfamiliar objects were congruent in terms of animacy (e.g., animal-like objects with words that described human). Participants then learned to associate objects and words that were either congruent or not in animacy, before receiving expertise training to rapidly individuate the objects. Congruent pairing of visual and conceptual information facilitated observers' ability to become a perceptual expert, as revealed in a matching task that required visual identification at the basic or subordinate levels. Taken together, these findings show that visual and conceptual information interact at multiple levels in objectrecognition. PMID:25120509

Objects contain rich visual and conceptual information, but do these two types of information interact? Here, we examine whether visual and conceptual information interact when observers see novel objects for the first time. We then address how this interaction influences the acquisition of perceptual expertise. We used two types of novel objects (Greebles), designed to resemble either animals or tools, and two lists of words, which described non-visual attributes of people or man-made objects. Participants first judged if a word was more suitable for describing people or objects while ignoring a task-irrelevant image, and showed faster responses if the words and the unfamiliar objects were congruent in terms of animacy (e.g., animal-like objects with words that described human). Participants then learned to associate objects and words that were either congruent or not in animacy, before receiving expertise training to rapidly individuate the objects. Congruent pairing of visual and conceptual information facilitated observers' ability to become a perceptual expert, as revealed in a matching task that required visual identification at the basic or subordinate levels. Taken together, these findings show that visual and conceptual information interact at multiple levels in objectrecognition. PMID:25120509

Face recognition is a natural human ability and a widely accepted identification and authentication method. In modern legal settings, a lot of credence is placed on identifications made by eyewitnesses. Consequently these are based on human perception which is often flawed and can lead to situations where identity is disputed. Therefore, there is a clear need to secure identifications in an objective way based on anthropometric measures. Anthropometry has existed for many years and has evolved with each advent of new technology and computing power. As a result of this, face recognition methodology has shifted from a purely 2D image-based approach to the use of 3D facial shape. However, one of the main challenges still remaining is the non-rigid structure of the face, which can change permanently over varying time-scales and briefly with facial expressions. The majority of face recognition methods have been developed by scientists with a very technical background such as biometry, pattern recognition and computer vision. This article strives to bridge the gap between these communities and the forensic science end-users. A concise review of face recognition using 3D shape is given. Methods using 3D shape applied to data embodying facial expressions are tabulated for reference. From this list a categorization of different strategies to deal with expressions is presented. The underlying concepts and practical issues relating to the application of each strategy are given, without going into technical details. The discussion clearly articulates the justification to establish archival, reference databases to compare and evaluate different strategies. PMID:20395086

The aim of this study was to determine the role of the dorsal dentate gyrus (dDG) in objectrecognition memory using a black box and object-context recognition memory using a clear box with available cues that define a spatial context. Based on a 10 min retention interval between the study phase and the test phase, the results indicated that dDG lesioned rats are impaired when compared to controls in the object-context recognition test in the clear box. However, there were no reliable differences between the dDG lesioned rats and the control group for the objectrecognition test in the black box. Even though the dDG lesioned rats were more active in object exploration, the habituation gradients did not differ. These results suggest that the dentate gyrus lesioned rats are clearly impaired when there is an important contribution of context. Furthermore, based on a 24 h retention interval in the black box the dDG lesioned rats were impaired compared to controls. PMID:23880567

Everyday vision requires robustness to a myriad of environmental factors that degrade stimuli. Foreground clutter can occlude objects of interest, and complex lighting and shadows can decrease the contrast of items. How does the brain recognize visual objects despite these low-quality inputs? On the basis of predictions from a model of objectrecognition that contains excitatory feedback, we hypothesized that recurrent processing would promote robust recognition when objects were degraded by strengthening bottom-up signals that were weakened because of occlusion and contrast reduction. To test this hypothesis, we used backward masking to interrupt the processing of partially occluded and contrast reduced images during a categorization experiment. As predicted by the model, we found significant interactions between the mask and occlusion and the mask and contrast, such that the recognition of heavily degraded stimuli was differentially impaired by masking. The model provided a close fit of these results in an isomorphic version of the experiment with identical stimuli. The model also provided an intuitive explanation of the interactions between the mask and degradations, indicating that masking interfered specifically with the extensive recurrent processing necessary to amplify and resolve highly degraded inputs, whereas less degraded inputs did not require much amplification and could be rapidly resolved, making them less susceptible to masking. Together, the results of the experiment and the accompanying model simulations illustrate the limits of feedforward vision and suggest that objectrecognition is better characterized as a highly interactive, dynamic process that depends on the coordination of multiple brain areas. PMID:22905822

Ear Recognition has recently received significant attention in the literature. Even though current ear recognitionsystems have reached a certain level of maturity, their success is still limited. This paper presents an efficient complete ear-based biometric system that can process five frames/sec; Hence it can be used for surveillance applications. The ear detection is achieved using Haar features arranged in a cascaded Adaboost classifier. The feature extraction is based on dividing the ear image into several blocks from which Local Binary Pattern feature distributions are extracted. These feature distributions are then fused at the feature level to represent the original ear texture in the classification stage. The contribution of this paper is three fold: (i) Applying a new technique for ear feature extraction, and studying various optimization parameters for that technique; (ii) Presenting a practical ear recognitionsystem and a detailed analysis about error propagation in that system; (iii) Studying the occlusion effect of several ear parts. Detailed experiments show that the proposed ear recognitionsystem achieved better performance (94:34%) compared to other shape-based systems as Scale-invariant feature transform (67:92%). The proposed approach can also handle efficiently hair occlusion. Experimental results show that the proposed system can achieve about (78%) rank-1 identification, even in presence of 60% occlusion.

To recognize that a picture is a representation of a real-life object is a cognitively demanding task. It requires an organism to mentally represent the concrete object (the picture) and abstract its relation to the item that it represents. This form of representational insight has been shown in a small number of mammal and bird species. However, it has not previously been studied in reptiles. This study examined picture-objectrecognition in the red-footed tortoise (Chelonoidis carbonaria). In Experiment 1, five red-footed tortoises were trained to distinguish between food and non-food objects using a two-alternative forced choice procedure. After reaching criterion, they were presented with test trials in which the real objects were replaced with color photographs of those objects. There was no difference in performance between training and test trials, suggesting that the tortoises did see some correspondence between the real object and its photographic representation. Experiment 2 examined the nature of this correspondence by presenting the tortoises with a choice between the real food object and a photograph of it. The findings revealed that the tortoises confused the photograph with the real-life object. This suggests that they process real items and photographic representations of these items in the same way and, in this context, do not exhibit representational insight. PMID:22945433

The design and development of a miniaturized optical processor that performs real time image correlation are described. The optical correlator utilizes the Vander Lugt matched spatial filter technique. The correlation output, a focused beam of light, is imaged onto a CMOS photodetector array. In addition to performing target recognition, the device also tracks the target. The hardware, composed of optical and electro-optical components, occupies only 590 cu cm of volume. A complete correlator system would also include an input imaging lens. This optical processing system is compact, rugged, requires only 3.5 watts of operating power, and weighs less than 3 kg. It represents a major achievement in miniaturizing optical processors. When considered as a special-purpose processing unit, it is an attractive alternative to conventional digital image recognition processing. It is conceivable that the combined technology of both optical and ditital processing could result in a very advanced robot vision system.

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond objectrecognition and tracking latencies. PMID:19635693

The paper considers the following problem: given a 3D model of a reference target and a sequence of images of a 3D scene, identify the object in the scene most likely to be the reference target and determine its current pose. Finding the best match in each frame independently of previous decisions is not optimal, since past information is ignored. Our solution concept uses a novel Bayesian framework for multi target tracking and objectrecognition to define and sequentially update the probability that the reference target is any one of the tracked objects. The approach is applied to problems of automatic lock-on and missile guidance using a laser radar seeker. Field trials have resulted in high target hit probabilities despite low resolution imagery and temporarily highly occluded targets.

Are face and objectrecognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing ["The Model", TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and objectrecognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces (separating them in representational space) that

Detecting objects in images containing strong clutter is an important issue in a variety of applications such as medical imaging and automatic target recognition. Artificial neural networks are used as non-parametric pattern recognizers to cope with different problems due to their inherent ability to learn from training data. In this paper we propose a neural approach based on the Random Neural Network model (Gelenbe 1989, 1990, 1991, 1993), to detect shaped targets with the help of multiple neural networks whose outputs are combined for making decisions.

The application of speech recognition technology in the Army command and control area is presented. The problems associated with this program are described as well as as its relevance in terms of the man/machine interactions, voice inflexions, and the amount of training needed to interact with and utilize the automated system.

As digital signal processors (DSPs) become more advanced, many real-time recognition problems will be solved with completely integrated solutions. In this paper a methodology which is designed for today's DSP architectures and is capable of addressing applications in real-time color objectrecognition is presented. The methodology is integrated into a processing structure called raster scan video processing which requires a small amount of memory. The small amount of memory required enables the entire recognitionsystem to be implemented on a single DSP. This auto-associative segmentation approach provides a means for desaturated color images to be segmented. The system is applied to the problem of stop sign recognition is realistically captured outdoor images.

Spectral similarity and spatial adjacency between various kinds of objects, shadow and occluded areas behind high rise objects as well as complex relationships lead to objectrecognition difficulties and ambiguities in complex urban areas. Using new multi-angular satellite imagery, higher levels of analysis and developing a context aware system may improve objectrecognition results in these situations. In this paper, the capability of multi-angular satellite imagery is used in order to solve objectrecognition difficulties in complex urban areas based on decision level fusion of Object Based Image Analysis (OBIA). The proposed methodology has two main stages. In the first stage, object based image analysis is performed independently on each of the multi-angular images. Then, in the second stage, the initial classified regions of each individual multi-angular image are fused through a decision level fusion based on the definition of scene context. Evaluation of the capabilities of the proposed methodology is performed on multi-angular WorldView-2 satellite imagery over Rio de Janeiro (Brazil).The obtained results represent several advantages of multi-angular imagery with respect to a single shot dataset. Together with the capabilities of the proposed decision level fusion method, most of the objectrecognition difficulties and ambiguities are decreased and the overall accuracy and the kappa values are improved.

In this paper we present a novel procedure for contour-based recognition of partially occluded three-dimensional objects. In our approach we use images of real and rendered objects whose contours have been deformed by a restricted change of the viewpoint. The preparatory part consists of contour extraction, preprocessing, local structure analysis and feature extraction. The main part deals with an extended construction and functionality of the classifier ensemble Adaptive Occlusion Classifier (AOC). It relies on a hierarchical fragmenting algorithm to perform a local structure analysis which is essential when dealing with occlusions. In the experimental part of this paper we present classification results for five classes of simple geometrical figures: prism, cylinder, half cylinder, a cube, and a bridge. We compare classification results for three classical feature extractors: Fourier descriptors, pseudo Zernike and Zernike moments.

Studies of patients with category-specific agnosia (CSA) have given rise to multiple theories of objectrecognition, most of which assume the existence of a stable, abstract semantic memory system. We applied an episodic view of memory to questions raised by CSA in a series of studies examining normal observers' recall of newly learned attributes…

Acetylcholine (ACh) has been implicated in numerous cognitive functions, including multisensory feature binding. In the present study, we systematically assessed the involvement of cholinergic muscarinic receptors in several variations of an objectrecognition task for rats. In the standard spontaneous objectrecognition (SOR) task, tactile and visual properties of objects were freely available throughout the sample and choice phases. In the tactile- and visual-only unimodal SOR tasks, exploration in both phases was restricted to tactile and visual information, respectively. For the basic crossmodal objectrecognition (CMOR) task, sample object exploration was limited to tactile features, whereas choice objects were available only in the visual domain. In Experiment 1, pre-sample systemic administration of scopolamine (0.2mg/kg) disrupted performance on standard SOR, both unimodal SOR tasks, and basic CMOR, consistent with a role for muscarinic receptors in memory encoding. Conversely, in Experiment 2, pre-choice systemic scopolamine selectively impaired objectrecognition on the CMOR task. For Experiment 3, the inclusion of multimodal, but not unimodal pre-exposure to the to-be-remembered objects prevented scopolamine from disrupting performance on the CMOR task when given prior to the choice phase. These results suggest that ACh is necessary during the choice phase of the CMOR task to facilitate the binding of object features across sensory modalities, a function that is not required for the other tasks assessed. Multimodal object pre-exposure might preclude the requisite contribution of ACh in the choice phase by allowing rats to bind important visual and tactile object information prior to testing. PMID:25490059

Long-term potentiation (LTP) phenomenon is widely accepted as a cellular model of memory consolidation. Objectrecognition (OR) is a particularly useful way of studying declarative memory in rodents because it makes use of their innate preference for novel over familiar objects. In this study, mice had electrodes implanted in the hippocampal Schaffer collaterals–pyramidal CA1 pathway and were trained for OR. Field EPSPs evoked at the CA3-CA1 synapse were recorded at the moment of training and at different times thereafter. LTP-like synaptic enhancement was found 6 h posttraining. A testing session was conducted 24 h after training, in the presence of one familiar and one novel object. Hippocampal synaptic facilitation was observed during exploration of familiar and novel objects. A short depotentiation period was observed early after the test and was followed by a later phase of synaptic efficacy enhancement. Here, we show that OR memory consolidation is accompanied by transient potentiation in the hippocampal CA3-CA1 synapses, while reconsolidation of this memory requires a short-lasting phase of depotentiation that could account for its well described vulnerability. The late synaptic enhancement phase, on the other hand, would be a consequence of memory restabilization. PMID:20133798

This paper describes the shape recognitionsystem that has been developed within the ESPRIT project 9052 ADAS on automatic disassembly of TV-sets using a robot cell. Depth data from a chirped laser radar are fused with color data from a video camera. The sensor data is pre-processed in several ways and the obtained representation is used to train a RAM neural network (memory based reasoning approach) to detect different components within TV-sets. The shape recognizing architecture has been implemented and tested in a demonstration setup.

In the current study, we examined how color knowledge in a domain of expertise influences the accuracy and speed of objectrecognition. In Experiment 1, expert bird-watchers and novice participants categorized common birds (e.g., robin, sparrow, cardinal) at the family level of abstraction. The bird images were shown in their natural congruent color, nonnatural incongruent color, and gray scale. The main finding was that color affected the performance of bird experts and bird novices, albeit in different ways. Although both experts and novices relied on color to recognize birds at the family level, analysis of the response time distribution revealed that color facilitated expert performance in the fastest and slowest trials whereas color only helped the novices in the slower trials. In Experiment 2, expert bird-watchers were asked to categorize congruent color, incongruent color, and gray scale images of birds at the more subordinate, species level (e.g., Nashville warbler, Wilson's warbler). The performance of experts was better with congruent color images than with incongruent color and gray scale images. As in Experiment 1, analysis of the response time distribution showed that the color effect was present in the fastest trials and was sustained through the slowest trials. Collectively, the findings show that experts have ready access to color knowledge that facilitates their fast and accurate identification at the family and species level of recognition. PMID:25113021

The objective of this project was to assess the performance of the leading commercial-off-the-shelf (COTS) facial recognition software package when used as a laptop application. We performed the assessment to determine the system's usefulness for enrolling facial images in a database from remote locations and conducting real-time searches against a database of previously enrolled images. The assessment involved creating a database of 40 images and conducting 2 series of tests to determine the product's ability to recognize and match subject faces under varying conditions. This report describes the test results and includes a description of the factors affecting the results. After an extensive market survey, we selected Visionics' FaceIt{reg_sign} software package for evaluation and a review of the Facial Recognition Vendor Test 2000 (FRVT 2000). This test was co-sponsored by the US Department of Defense (DOD) Counterdrug Technology Development Program Office, the National Institute of Justice, and the Defense Advanced Research Projects Agency (DARPA). Administered in May-June 2000, the FRVT 2000 assessed the capabilities of facial recognitionsystems that were currently available for purchase on the US market. Our selection of this Visionics product does not indicate that it is the ''best'' facial recognition software package for all uses. It was the most appropriate package based on the specific applications and requirements for this specific application. In this assessment, the system configuration was evaluated for effectiveness in identifying individuals by searching for facial images captured from video displays against those stored in a facial image database. An additional criterion was that the system be capable of operating discretely. For this application, an operational facial recognitionsystem would consist of one central computer hosting the master image database with multiple standalone systems configured with duplicates of the master operating in

The process of recognition, for instance, understanding the text, written by different fonts, consists in the depriving of the individual attributes of the letters in the particular font. It is shown that such process, in Nature and technique, can be provided by the narrowing the spatial frequency of the object's image by its defocusing. In defocusing images remain only areas, so-called Informative Fragments (IFs), which all together form the generalized (stylized) image of many identical objects. It is shown that the variety of shapes of IFs is restricted and can be presented by `Geometrical alphabet'. The `letters' for this alphabet can be created using two basic `genetic' figures: a stripe and round spot. It is known from physiology that the special cells of visual cortex response to these particular figures. The prototype of such `genetic' alphabet has been made using Boolean algebra (Venn's diagrams). The algorithm for drawing the letter's (`genlet's') shape in this alphabet and generalized images of objects (for example, `sleeping cat'), are given. A scheme of an anthropomorphic robot is shown together with results of model computer experiment of the robot's action--`drawing' the generalized image.

Findings have shown that histamine receptors in the hippocampus modulate the acquisition and extinction of fear motivated learning. In order to determine the role of hippocampal histaminergic receptors on recognition memory, adult male Wistar rats with indwelling infusion cannulae stereotaxically placed in the CA1 region of dorsal hippocampus were trained in an objectrecognition learning task involving exposure to two different stimulus objects in an enclosed environment. In the test session, one of the objects presented during training was replaced by a novel one. Recognition memory retention was assessed 24 h after training by comparing the time spent in exploration (sniffing and touching) of the known object with that of the novel one. When infused in the CA1 region immediately, 30, 120 or 360 min posttraining, the H1-receptor antagonist, pyrilamine, the H2-receptor antagonist, ranitidine, and the H3-receptor agonist, imetit, blocked long-term memory retention in a time dependent manner (30-120 min) without affecting general exploratory behavior, anxiety state or hippocampal function. Our data indicate that histaminergic system modulates consolidation of objectrecognition memory through H1, H2 and H3 receptors. PMID:23583502

A main criterion for comparison and selection of thermal imagers for military applications is their nominal range performance. This nominal range performance is calculated for a defined task and standardized target and environmental conditions. The only standardization available to date is STANAG 4347. The target defined there is based on a main battle tank in front view. Because of modified military requirements, this target is no longer up-to-date. Today, different topics of interest are of interest, especially differentiation between friend and foe and identification of humans. There is no direct way to differentiate between friend and foe in asymmetric scenarios, but one clue can be that someone is carrying a weapon. This clue can be transformed in the observer tasks detection: a person is carrying or is not carrying an object, recognition: the object is a long / medium / short range weapon or civil equipment and identification: the object can be named (e. g. AK-47, M-4, G36, RPG7, Axe, Shovel etc.). These tasks can be assessed experimentally and from the results of such an assessment, a standard target for handheld objects may be derived. For a first assessment, a human carrying 13 different handheld objects in front of his chest was recorded at four different ranges with an IR-dual-band camera. From the recorded data, a perception experiment was prepared. It was conducted with 17 observers in a 13-alternative forced choice, unlimited observation time arrangement. The results of the test together with Minimum Temperature Difference Perceived measurements of the camera and temperature difference and critical dimension derived from the recorded imagery allowed defining a first standard target according to the above tasks. This standard target consist of 2.5 / 3.5 / 5 DRI line pairs on target, 0.24 m critical size and 1 K temperature difference. The values are preliminary and have to be refined in the future. Necessary are different aspect angles, different

Over the years imaging laser radar systems have been developed for both military and civilian (topographic) applications. Among the applications, 3D data is used for environment modeling and object reconstruction and recognition. The data processing methods are mainly developed separately for military or topographic applications, seldom both application areas are in mind. In this paper, an overview of methods from both areas is presented. First, some of the work on ground surface estimation and classification of natural objects, for example trees, is described. Once natural objects have been detected and classified, we review some of the extensive work on reconstruction and recognition of man-made objects. Primarily we address the reconstruction of buildings and recognition of vehicles. Further, some methods for evaluation of measurement systems and algorithms are described. Models of some types of laser radar systems are reviewed, based on both physical and statistical approaches, for analysis and evaluation of measurement systems and algorithms. The combination of methods for reconstruction of natural and man-made objects is also discussed. By combining methods originating from civilian and military applications, we believe that the tools to analyze a whole scene become available. In this paper we show examples where methods from both application fields are used to analyze a scene.

The present work examined whether post-training systemic epinephrine (EPI) is able to modulate short-term (3h) and long-term (24 h and 48 h) memory of standard objectrecognition, as well as long-term (24 h) memory of separate "what" (object identity) and "where" (object location) components of objectrecognition. Although objectrecognition training is associated to low arousal levels, all the animals received habituation to the training box in order to further reduce emotional arousal. Post-training EPI improved long-term (24 h and 48 h), but not short-term (3 h), memory in the standard objectrecognition task, as well as 24 h memory for both object identity and object location. These data indicate that post-training epinephrine: (1) facilitates long-term memory for standard objectrecognition; (2) exerts separate facilitatory effects on "what" (object identity) and "where" (object location) components of objectrecognition; and (3) is capable of improving memory for a low arousing task even in highly habituated rats. PMID:19788899

The market for real-time 3-D mapping includes not only traditional geospatial applications but also navigation of unmanned autonomous vehicles (UAVs). Massively parallel processes such as graphics processing unit (GPU) computing make real-time 3-D objectrecognition and mapping achievable. Geospatial technologies such as digital photogrammetry and GIS offer advanced capabilities to produce 2-D and 3-D static maps using UAV data. The goal is to develop real-time UAV navigation through increased automation. It is challenging for a computer to identify a 3-D object such as a car, a tree or a house, yet automatic 3-D objectrecognition is essential to increasing the productivity of geospatial data such as 3-D city site models. In the past three decades, researchers have used radiometric properties to identify objects in digital imagery with limited success, because these properties vary considerably from image to image. Consequently, our team has developed software that recognizes certain types of 3-D objects within 3-D point clouds. Although our software is developed for modeling, simulation and visualization, it has the potential to be valuable in robotics and UAV applications. The locations and shapes of 3-D objects such as buildings and trees are easily recognizable by a human from a brief glance at a representation of a point cloud such as terrain-shaded relief. The algorithms to extract these objects have been developed and require only the point cloud and minimal human inputs such as a set of limits on building size and a request to turn on a squaring option. The algorithms use both digital surface model (DSM) and digital elevation model (DEM), so software has also been developed to derive the latter from the former. The process continues through the following steps: identify and group 3-D object points into regions; separate buildings and houses from trees; trace region boundaries; regularize and simplify boundary polygons; construct complex roofs. Several case

Some problems in the application of Ladar reflective tomography for space objectrecognition are studied in this work. An analytic target model is adopted to investigate the image reconstruction properties with limited relative angle range, which are useful to verify the target shape from the incomplete image, analyze the shadowing effect of the target and design the satellite payloads against recognition via reflective tomography approach. We proposed an iterative maximum likelihood method basing on Bayesian theory, which can effectively compress the pulse width and greatly improve the image resolution of incoherent LRT system without loss of signal to noise ratio.

In this paper we solve the problem of pose recognition of a 3D object in non-uniformly illuminated and noisy scenes. The recognitionsystem employs a bank of space-variant correlation filters constructed with an adaptive approach based on local statistical parameters of the input scene. The position and orientation of the target are estimated with the help of the filter bank. For an observed input frame, the algorithm computes the correlation process between the observed image and the bank of filters using a combination of data and task parallelism by taking advantage of a graphics processing unit (GPU) architecture. The pose of the target is estimated by finding the template that better matches the current view of target within the scene. The performance of the proposed system is evaluated in terms of recognition accuracy, location and orientation errors, and computational performance.

Invariant objectrecognition, which means the recognition of object categories independent of conditions like viewing angle, scale and illumination, is a task of great interest that humans can fulfill much better than artificial systems. During the last years several basic principles were derived from neurophysiological observations and careful consideration: (1) Developing invariance to possible transformations of the object by learning temporal sequences of visual features that occur during the respective alterations. (2) Learning in a hierarchical structure, so basic level (visual) knowledge can be reused for different kinds of objects. (3) Using feedback to compare predicted input with the current one for choosing an interpretation in the case of ambiguous signals. In this paper we propose a network which implements all of these concepts in a computationally efficient manner which gives very good results on standard object datasets. By dynamically switching off weakly active neurons and pruning weights computation is sped up and thus handling of large databases with several thousands of images and a number of categories in a similar order becomes possible. The involved parameters allow flexible adaptation to the information content of training data and allow tuning to different databases relatively easily. Precondition for successful learning is that training images are presented in an order assuring that images of the same object under similar viewing conditions follow each other. Through an implementation with sparse data structures the system has moderate memory demands and still yields very good recognition rates. PMID:24657573

Work-related exposure to noise and other ototoxins can cause damage to the cochlea, synapses between the inner hair cells, the auditory nerve fibers, and higher auditory pathways, leading to difficulties in recognizing speech. Procedures designed to determine speech recognition scores (SRS) in an objective manner can be helpful in disability compensation cases where the worker claims to have poor speech perception due to exposure to noise or ototoxins. Such measures can also be helpful in determining SRS in individuals who cannot provide reliable responses to speech stimuli, including patients with Alzheimer's disease, traumatic brain injuries, and infants with and without hearing loss. Cost-effective neural monitoring hardware and software is being rapidly refined due to the high demand for neurogaming (games involving the use of brain-computer interfaces), health, and other applications. More specifically, two related advances in neuro-technology include relative ease in recording neural activity and availability of sophisticated analysing techniques. These techniques are reviewed in the current article and their applications for developing objective SRS procedures are proposed. Issues related to neuroaudioethics (ethics related to collection of neural data evoked by auditory stimuli including speech) and neurosecurity (preservation of a person's neural mechanisms and free will) are also discussed. PMID:26807789

Williams syndrome (WS) is a genetic disorder associated with severe visuospatial deficits, relatively strong language skills, heightened social interest, and increased attention to faces. On the basis of the visuospatial impairments, this disorder has been characterized primarily as a deficit of the dorsal stream, the occipitoparietal brain regions that subserve visuospatial processing. However, some evidence indicates that this disorder may also affect the development of the ventral stream, the occipitotemporal cortical regions that subserve face and objectrecognition. The present studies examined ventral stream function in WS, with the hypothesis that faces would produce a relatively more mature pattern of ventral occipitotemporal cortical activation, relative to other objects that are also represented across these visual areas. We compared functional magnetic resonance imaging activation patterns during viewing of human faces, cat faces, houses and shoes in individuals with WS (age 14–27), typically developing 6–9 year olds (matched approximately on mental age), and typically developing 14–26 year olds (matched on chronological age). Typically developing individuals exhibited changes in the pattern of activation over age, consistent with previous reports. The ventral stream topography of the WS individuals differed from both control groups, however, reflecting the same level of activation to face stimuli as chronological age matches, but less activation to house stimuli than either mental age or chronological age matches. We discuss the possible causes of this unusual topography and implications for understanding the behavioral profile of people with WS. PMID:21477194

Most animals use multiple sensory modalities to obtain information about objects in their environment. There is a clear adaptive advantage to being able to recognize objects cross-modally and spontaneously (without prior training with the sense being tested) as this increases the flexibility of a multisensory system, allowing an animal to perceive its world more accurately and react to environmental changes more rapidly. So far, spontaneous cross-modal objectrecognition has only been shown in a few mammalian species, raising the question as to whether such a high-level function may be associated with complex mammalian brain structures, and therefore absent in animals lacking a cerebral cortex. Here we use an object-discrimination paradigm based on operant conditioning to show, for the first time to our knowledge, that a nonmammalian vertebrate, the weakly electric fish Gnathonemus petersii, is capable of performing spontaneous cross-modal objectrecognition and that the sensory inputs are weighted dynamically during this task. We found that fish trained to discriminate between two objects with either vision or the active electric sense, were subsequently able to accomplish the task using only the untrained sense. Furthermore we show that cross-modal objectrecognition is influenced by a dynamic weighting of the sensory inputs. The fish weight object-related sensory inputs according to their reliability, to minimize uncertainty and to enable an optimal integration of the senses. Our results show that spontaneous cross-modal objectrecognition and dynamic weighting of sensory inputs are present in a nonmammalian vertebrate. PMID:27313211

This paper presents a novel system that is fusing efficient and state-of-the-art techniques of stereo vision and machine learning, aiming at object detection and recognition. To this goal, the system initially creates depth maps by employing the Graph-Cut technique. Then, the depth information is used for object detection by separating the objects from the whole scene. Next, the Scale-Invariant Feature Transform (SIFT) is used, providing the system with unique object's feature key-points, which are employed in training an Artificial Neural Network (ANN). The system is then able to classify and recognize the nature of these objects, creating knowledge from the real world. [Figure not available: see fulltext.

Recognition of an object usually involves a wide range of sensory inputs. Accumulating evidence shows that first brain responses associated with the visual discrimination of objects emerge around 150 ms, but fewer studies have been devoted to measure the first neural signature of haptic recognition. To investigate the speed of haptic processing, we recorded event-related potentials (ERPs) during a shape discrimination task without visual information. After a restricted exploratory procedure, participants (n = 27) were instructed to judge whether the touched object corresponded to an expected object whose name had been previously presented in a screen. We encountered that any incongruence between the presented word and the shape of the object evoked a frontocentral negativity starting at ∼175 ms. With the use of source analysis and L2 minimum-norm estimation, the neural sources of this differential activity were located in higher level somatosensory areas and prefrontal regions involved in error monitoring and cognitive control. Our findings reveal that the somatosensory system is able to complete an amount of haptic processing substantial enough to trigger conflict-related responses in medial and prefrontal cortices in <200 ms. The present results show that our haptic system is a fast recognition device closely interlinked with error- and conflict-monitoring processes. PMID:25744887

A method is provided for controlling two objects relatively moveable with respect to each other. A plurality of receivers are provided for detecting a distinctive microwave signal from each of the objects and measuring the phase thereof with respect to a reference signal. The measured phase signal is used to determine a distance between each of the objects and each of the plurality of receivers. Control signals produced in response to the relative distances are used to control the position of the two objects.

In crowding, the perception of a target strongly deteriorates when neighboring elements are presented. Crowding is usually assumed to have the following characteristics. (a) Crowding is determined only by nearby elements within a restricted region around the target (Bouma's law). (b) Increasing the number of flankers can only deteriorate performance. (c) Target-flanker interference is feature-specific. These characteristics are usually explained by pooling models, which are well in the spirit of classic models of objectrecognition. In this review, we summarize recent findings showing that crowding is not determined by the above characteristics, thus, challenging most models of crowding. We propose that the spatial configuration across the entire visual field determines crowding. Only when one understands how all elements of a visual scene group with each other, can one determine crowding strength. We put forward the hypothesis that appearance (i.e., how stimuli look) is a good predictor for crowding, because both crowding and appearance reflect the output of recurrent processing rather than interactions during the initial phase of visual processing. PMID:26024452

The objectrecognition task is a procedure based on rodents' natural tendency to explore novel objects which is frequently used for memory testing. However, in some instances novelty preference is replaced by familiarity preference, raising questions regarding the validity of novelty preference as a pure recognition memory index. Acute stress- and corticosterone administration-induced novel object preference disruption has been frequently interpreted as memory impairment; however, it is still not clear whether such effect can be actually attributed to either mnemonic disruption or altered novelty seeking. Seventy-five adult male Wistar rats were trained in an objectrecognition task and subjected to either acute stress or corticosterone administration to evaluate the effect of stress or corticosterone on an objectrecognition task. Acute stress was induced by restraining movement for 1 or 4h, ending 30 min before the sample trial. Corticosterone was injected intraperitoneally 10 min before the test trial which was performed either 1 or 24h after the sample trial. Four-hour, but not 1-h, stress induced familiar object preference during the test trial performed 1h after the sample trial; however, acute stress had no effects on the test when performed 24h after sample trial. Systemic administration of corticosterone before the test trial performed either 1 or 24h after the sample trial also resulted in familiar object preference. However, neither acute stress nor corticosterone induced changes in locomotor behaviour. Taken together, such results suggested that acute stress probably does not induce memory retrieval impairment but, instead, induces an emotional arousing state which motivates novelty avoidance. PMID:25986403

The novel objectrecognition (NOR) test has been widely used to test memory function. We developed a 3D computerized video analysis system that estimates nose contact with an object in Long Evans rats to analyze object exploration during NOR tests. The results indicate that the 3D system reproducibly and accurately scores the NOR test. Furthermore, the 3D system captures a 3D trajectory of the nose during object exploration, enabling detailed analyses of spatiotemporal patterns of object exploration. The 3D trajectory analysis revealed a specific pattern of object exploration in the sample phase of the NOR test: normal rats first explored the lower parts of objects and then gradually explored the upper parts. A systematic injection of MK-801 suppressed changes in these exploration patterns. The results, along with those of previous studies, suggest that the changes in the exploration patterns reflect neophobia to a novel object and/or changes from spatial learning to object learning. These results demonstrate that the 3D tracking system is useful not only for detailed scoring of animal behaviors but also for investigation of characteristic spatiotemporal patterns of object exploration. The system has the potential to facilitate future investigation of neural mechanisms underlying object exploration that result from dynamic and complex brain activity. PMID:24991752

Different processes are assumed to underlie invariant objectrecognition across affine transformations, such as changes in size, and non-affine transformations, such as rotations in depth. From this perspective, promoting invariant objectrecognition across rotations in depth requires visual experience with the object from multiple viewpoints. One learning mechanism potentially contributing to invariant recognition is the error-driven learning of associations between relatively view-invariant visual properties and motor responses or object labels. This account uniquely predicts that experience with affine transformations of a single object view may also promote view-invariance, if view-invariant properties are also invariant across such affine transformations. We empirically confirmed this prediction in both people and pigeons, thereby suggesting that: (a) the hypothesized mechanism participates in view-invariance learning, (b) this mechanism is present across distantly-related vertebrates, and (c) the distinction between affine and non-affine transformations may not be fundamental for biological visual systems, as previously assumed. PMID:26608549

Atmospheric turbulence adds accumulated distortion to images obtained by cameras and surveillance systems. When the turbulence grows stronger or when the object is further away from the observer, increasing the recording device resolution helps little to improve the quality of the image. Many sophisticated methods to correct the distorted images have been invented, such as using a known feature on or near the target object to perform a deconvolution process, or use of adaptive optics. However, most of the methods depend heavily on the object's location, and optical ray propagation through the turbulence is not directly considered. Alternatively, selecting a lucky image over many frames provides a feasible solution, but at the cost of time. In our work, we propose an innovative approach to improving image quality through turbulence by making use of a modified plenoptic camera. This type of camera adds a micro-lens array to a traditional high-resolution camera to form a semi-camera array that records duplicate copies of the object as well as "superimposed" turbulence at slightly different angles. By performing several steps of image reconstruction, turbulence effects will be suppressed to reveal more details of the object independently (without finding references near the object). Meanwhile, the redundant information obtained by the plenoptic camera raises the possibility of performing lucky image algorithmic analysis with fewer frames, which is more efficient. In our work, the details of our modified plenoptic cameras and image processing algorithms will be introduced. The proposed method can be applied to coherently illuminated object as well as incoherently illuminated objects. Our result shows that the turbulence effect can be effectively suppressed by the plenoptic camera in the hardware layer and a reconstructed "lucky image" can help the viewer identify the object even when a "lucky image" by ordinary cameras is not achievable.

Performance during objectrecognition across views is largely dependent on inter-object similarity. The present study was designed to investigate the similarity dependency of objectrecognition learning on the changes in ERP component N1. Human subjects were asked to train themselves to recognize novel objects with different inter-object similarity by performing objectrecognition tasks. During the tasks, images of an object had to be discriminated from the images of other objects irrespective of the viewpoint. When objects had a high inter-object similarity, the ERP component, N1 exhibited a significant increase in both the amplitude and the latency variation across objects during the objectrecognition learning process, and the N1 amplitude and latency variation across the views of the same objects decreased significantly. In contrast, no significant changes were found during the learning process when using objects with low inter-object similarity. The present findings demonstrate that the changes in the variation of N1 that accompany the objectrecognition learning process are dependent upon the inter-object similarity and imply that there is a difference in the neuronal representation for objectrecognition when using objects with high and low inter-object similarity. PMID:22115890

In the research project THESEUS MEDICO we aim at a system combining medical image information with semantic background knowledge from ontologies to give clinicians fully cross-modal access to biomedical image repositories. Therefore joint efforts have to be made in more than one dimension: Object detection processes have to be specified in which an abstraction is performed starting from low-level image features across landmark detection utilizing abstract domain knowledge up to high-level objectrecognition. We propose a system based on a client-server extension of the scientific workflow platform Kepler that assists the collaboration of medical experts and computer scientists during development and parameter learning.

The ovarian hormones 17β-estradiol (E2) and progesterone (P4) are potent modulators of hippocampal memory formation. Both hormones have been demonstrated to enhance hippocampal memory by regulating the cellular and molecular mechanisms thought to underlie memory formation. Behavioral neuroendocrinologists have increasingly used the objectrecognition and object placement (object location) tasks to investigate the role of E2 and P4 in regulating hippocampal memory formation in rodents. These one-trial learning tasks are ideal for studying acute effects of hormone treatments on different phases of memory because they can be administered during acquisition (pre-training), consolidation (post-training), or retrieval (pre-testing). This review synthesizes the rodent literature testing the effects of E2 and P4 on objectrecognition (OR) and object placement (OP), and the molecular mechanisms in the hippocampus supporting memory formation in these tasks. Some general trends emerge from the data. Among gonadally intact females, object memory tends to be best when E2 and P4 levels are elevated during the estrous cycle, pregnancy, and in middle age. In ovariectomized females, E2 given before or immediately after testing generally enhances OR and OP in young and middle-aged rats and mice, although effects are mixed in aged rodents. Effects of E2 treatment on OR 7and OP memory consolidation can be mediated by both classical estrogen receptors (ERα and ERβ), and depend on glutamate receptors (NMDA, mGluR1) and activation of numerous cell signaling cascades (e.g., ERK, PI3K/Akt, mTOR) and epigenetic processes (e.g., histone H3 acetylation, DNA methylation). Acute P4 treatment given immediately after training also enhances OR and OP in young and middle-aged ovariectomized females by activating similar cell signaling pathways as E2 (e.g., ERK, mTOR). The few studies that have administered both hormones in combination suggest that treatment can enhance OR and OP, but that

The deployment of fingerprint recognitionsystems has always raised concerns related to personal privacy. A fingerprint is permanently associated with an individual and, generally, it cannot be reset if compromised in one application. Given that fingerprints are not a secret, potential misuses besides personal recognition represent privacy threats and may lead to public distrust. Privacy mechanisms control access to personal information and limit the likelihood of intrusions. In this paper, image- and feature-level schemes for privacy protection in fingerprint recognitionsystems are reviewed. Storing only key features of a biometric signature can reduce the likelihood of biometric data being used for unintended purposes. In biometric cryptosystems and biometric-based key release, the biometric component verifies the identity of the user, while the cryptographic key protects the communication channel. Transformation-based approaches only a transformed version of the original biometric signature is stored. Different applications can use different transforms. Matching is performed in the transformed domain which enable the preservation of low error rates. Since such templates do not reveal information about individuals, they are referred to as cancelable templates. A compromised template can be re-issued using a different transform. At image-level, de-identification schemes can remove identifiers disclosed for objectives unrelated to the original purpose, while permitting other authorized uses of personal information. Fingerprint images can be de-identified by, for example, mixing fingerprints or removing gender signature. In both cases, degradation of matching performance is minimized.

In a previous study, we reported apparently paradoxical facilitation of objectrecognition memory following infusions of the cholinergic muscarinic receptor antagonist scopolamine into the perirhinal cortex (PRh) of rats. We attributed these effects to the blockade by scopolamine of the acquisition of interfering information. The present study tested this possibility directly by modifying the spontaneous objectrecognition memory task to allow the presentation of a potentially interfering object either before the sample phase or in the retention delay between the sample and choice phases. Presentation of an object between the sample and choice phases disrupted subsequent recognition of the sample object (retroactive interference), and intra-PRh infusions of scopolamine prior to the presentation of the irrelevant object prevented this retroactive interference effect. Moreover, presentation of an irrelevant object prior to the sample phase interfered proactively with sample objectrecognition, and intra-PRh infusions of scopolamine prior to the presentation of the pre-sample object prevented this proactive interference effect. These results suggest that blocking muscarinic cholinergic receptors in PRh can disrupt the acquisition of potentially interfering object information, thereby facilitating objectrecognition memory. This finding provides further, strong evidence that acetylcholine is important for the acquisition of object information in PRh. PMID:17823242

Our objectrecognition abilities, a direct product of our experience with objects, are fine-tuned to perfection. Left temporal and lateral areas along the dorsal, action related stream, as well as left infero-temporal areas along the ventral, object related stream are engaged in objectrecognition. Here we show that expertise modulates the activity of dorsal areas in the recognition of man-made objects with clearly specified functions. Expert chess players were faster than chess novices in identifying chess objects and their functional relations. Experts' advantage was domain-specific as there were no differences between groups in a control task featuring geometrical shapes. The pattern of eye movements supported the notion that experts' extensive knowledge about domain objects and their functions enabled superior recognition even when experts were not directly fixating the objects of interest. Functional magnetic resonance imaging (fMRI) related exclusively the areas along the dorsal stream to chess specific objectrecognition. Besides the commonly involved left temporal and parietal lateral brain areas, we found that only in experts homologous areas on the right hemisphere were also engaged in chess specific objectrecognition. Based on these results, we discuss whether skilled objectrecognition does not only involve a more efficient version of the processes found in non-skilled recognition, but also qualitatively different cognitive processes which engage additional brain areas. PMID:21283683

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and objectrecognition depends on experience with objects. In 256 subjects we measured face recognition, objectrecognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor objectrecognition but moderated their relationship: Face recognition performance is increasingly similar to objectrecognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and objectrecognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. PMID:24993021

In order to improve the recognition accuracy among the kinds of aphids in the complex backgrounds, the recognition method among kinds of aphids based on Dual-Tree Complex Wavelet Transform (DT-CWT) and Support Vector Machine (Libsvm) is proposed. Firstly the image is pretreated; secondly the aphid images' texture feature of three crops are extracted by DT-CWT in order to get the training parameters of training model; finally the training model could recognize aphids among the three kinds of crops. By contrasting to Gabor wavelet transform and the traditional extracting texture's methods based on Gray-Level Co-Occurrence Matrix (GLCM), the experiment result shows that the method has a certain practicality and feasibility and provides basic for aphids' recognition between the identification among same kind aphid.

Research has provided strong evidence of multisensory convergence of visual and haptic information within the visual cortex. These studies implement crossmodal matching paradigms to examine how systems use information from different sensory modalities for objectrecognition. Developmentally, behavioral evidence of visuohaptic crossmodal processing has suggested that communication within sensory systems develops earlier than across systems; nonetheless, it is unknown how the neural mechanisms driving these behavioral effects develop. To address this gap in knowledge, BOLD functional Magnetic Resonance Imaging (fMRI) was measured during delayed match-to-sample tasks that examined intramodal (visual-to-visual, haptic-to-haptic) and crossmodal (visual-to-haptic, haptic-to-visual) novel objectrecognition in children aged 7-8.5 years and adults. Tasks were further divided into sample encoding and test matching phases to dissociate the relative contributions of each. Results of crossmodal and intramodal objectrecognition revealed the network of known visuohaptic multisensory substrates, including the lateral occipital complex (LOC) and the intraparietal sulcus (IPS). Critically, both adults and children showed crossmodal enhancement within the LOC, suggesting a sensitivity to changes in sensory modality during recognition. These groups showed similar regions of activation, although children generally exhibited more widespread activity during sample encoding and weaker BOLD signal change during test matching than adults. Results further provided evidence of a bilateral region in the occipitotemporal cortex that was haptic-preferring in both age groups. This region abutted the bimodal LOtv, and was consistent with a medial to lateral organization that transitioned from a visual to haptic bias within the LOC. These findings converge with existing evidence of visuohaptic processing in the LOC in adults, and extend our knowledge of crossmodal processing in adults and

For humans, retinal images provide sufficient information for the complete understanding of three-dimensional shapes in a scene. The ultimate goal of computer vision is to develop an automated system able to reproduce some of the tasks performed in a natural way by human beings as recognition, classification, or analysis of the environment as basis for further decisions. At the first level, referred to as early computer vision, the task is to extract symbolic descriptive information in a scene from a variety of sensory data. The second level is concerned with classification, recognition, or decision systems and the related heuristics, that aid the processing of the available information. This research is concerned with a new approach to 3-D object representation and recognition using an interpolation scheme applied to the information from the fusion of range and intensity data. The range image acquisition uses a methodology based on a passive stereo-vision model originally developed to be used with a sequence of images. However, curved features, large disparities and noisy input images are some of the problems associated with real imagery, which need to be addressed prior to applying the matching techniques in the spatial frequency domain. Some of the above mentioned problems can only be solved by computationally intensive spatial domain algorithms. Regularization techniques are explored for surface recovery from sparse range data, and intensity images are incorporated in the final representation of the surface. As an important application, the problem of 3-D representation of retinal images for extraction of quantitative information is addressed. Range information is also combined with intensity data to provide a more accurate numerical description based on aspect graphs. This representation is used as input to a three-dimensional objectrecognitionsystem. Such an approach results in an improved performance of 3-D object classifiers.

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of objectrecognitionsystems. Therefore, this paper presents a novel approach for fast and robust objectrecognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the objectrecognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for objectrecognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the objectrecognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. As there is no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India. In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten Kannada character is resized into 20x30 Pixel. The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different Kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognitionsystems.

Dysfunctions in the perirhinal cortex (PRh) are associated with visual recognition memory deficit, which is frequently detected in the early stage of Alzheimer's disease. Muscarinic acetylcholine receptor-dependent long-term depression (mAChR-LTD) of synaptic transmission is known as a key pathway in eliciting this type of memory, and Tg2576 mice expressing enhanced levels of Aβ oligomers are found to have impaired mAChR-LTD in this brain area at as early as 3 months of age. We found that the administration of Aβ oligomers in young normal mice also induced visual recognition memory impairment and perturbed mAChR-LTD in mouse PRh slices. In addition, when mice were treated with infliximab, a monoclonal antibody against TNF-α, visual recognition memory impaired by pre-administered Aβ oligomers dramatically improved and the detrimental Aβ effect on mAChR-LTD was annulled. Taken together, these findings suggest that Aβ-induced inflammation is mediated through TNF-α signaling cascades, disturbing synaptic transmission in the PRh, and leading to visual recognition memory deficits. PMID:27265784

Objectrecognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant objectrecognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant objectrecognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) require visual experience with slowly changing objects to develop invariant objectrecognition abilities. When newborn chicks were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant objectrecognition. These results indicate that invariant objectrecognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world. PMID:27097925

Research on the role of the hippocampus in objectrecognition memory has produced conflicting results. Previous studies have used permanent hippocampal lesions to assess the requirement for the hippocampus in the objectrecognition task. However, permanent hippocampal lesions may impact performance through effects on processes besides memory…

These experiments investigated the involvement of several temporal lobe regions in consolidation of recognition memory. Anisomycin, a protein synthesis inhibitor, was infused into the hippocampus, perirhinal cortex, insular cortex, or basolateral amygdala of rats immediately after the sample phase of object or object-in-context recognition memory…

Since 1970's, the need of an automatic license plate recognitionsystem, sometimes referred as Automatic License Plate Recognitionsystem, has been increasing. A license plate recognitionsystem is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognitionsystems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognitionsystem is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognitionsystem is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognitionsystem that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.

Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification. The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

Previous studies have suggested that face identification is more sensitive to variations in spatial frequency content than objectrecognition, but none have compared how sensitive the 2 processes are to variations in spatial frequency overlap (SFO). The authors tested face and object matching accuracy under varying SFO conditions. Their results…

Proposed a method for detection of flat objects when they overlap condition. The method is based on two separate recognition algorithms flat objects. The first algorithm is based on a binary image of the signature of the object plane. The second algorithm is based on the values of the discrete points in the curvature contour of a binary image. The results of experimental studies of algorithms and a method of recognition of individual superimposed flat objects.

Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. PMID:23644160

The ability to recognize common objects from sparse information about geometric shape emerges during the same period in which children learn object names and object categories. Hummel and Biederman's (1992) theory of objectrecognition proposes that the geometric shapes of objects have two components--geometric volumes representing major object…

Recently, many researchers tackle accurate objectrecognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate objectrecognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1, 132, 038 transistors and the die area is 2,195µm × 1,985µm under a 0.180µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65MHz operation frequency.

This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Objectrecognition and infrastructure analysis. Objectrecognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the objectrecognition mode and the infrastructure analysis mode. The aim of the objectrecognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The

The paper formulates the mathematical foundations of object discrimination and object re-identification in range image sequences using Bayesian decision theory. Object discrimination determines the unique model corresponding to each scene object, while object re-identification finds the unique object in the scene corresponding to a given model. In the first case object identities are independent; in the second case at most one object exists having a given identity. Efficient analytical and numerical techniques for updating and maximizing the posterior distributions are introduced. Experimental results indicate to what extent a single range image of an object can be used for re-identifying this object in arbitrary scenes. Applications including the protection of commercial vessels against piracy are discussed.

Real-time measurement using multi-camera 3D measuring system requires three major components to operate at high speed: image data processing; correspondence; and least squares estimation. This paper is based upon a system developed at City University which uses high speed solutions for the first and last elements, and describes recent work to provide a high speed solution to the correspondence problem. Correspondence has traditionally been solved in photogrammetry by using human stereo fusion of two views of an object providing an immediate solution. Computer vision researchers and photogrammetrists have applied image processing techniques and computers to the same configuration and have developed numerous matching algorithms with considerable success. Where research is still required, and the published work is not so plentiful, is in the area of multi-camera correspondence. The most commonly used methods utilize the epipolar geometry to establish the correspondences. While this method is adequate for some simple situations, extensions to more than just a few cameras are required which are reliable and efficient. In this paper the early stages of research into reliable and efficient multi-camera correspondence method for high speed measurement tasks are reported.

Two recent lines of research suggest that explicitly naming objects at study influences subsequent memory for those objects at test. Lupyan (2008) suggested that naming "impairs" memory by a representational shift of stored representations of named objects toward the prototype (labeling effect). MacLeod, Gopie, Hourihan, Neary, and Ozubko (2010)…

Object substitutions in play (e.g. using a box as a car) are strongly linked to language learning and their absence is a diagnostic marker of language delay. Classic accounts posit a symbolic function that underlies both words and object substitutions. Here we show that object substitutions depend on developmental changes in visual object…

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Two experiments examined developmental changes in children’s visual recognition of common objects during the period of 18 to 24 months. Experiment 1 examined children’s ability to recognize common category instances that presented three different kinds of information: (1) richly detailed and prototypical instances that presented both local and global shape information, color, textural and featural information, (2) the same rich and prototypical shapes but no color, texture or surface featural information, or (3) that presented only abstract and global representations of object shape in terms of geometric volumes. Significant developmental differences were observed only for the abstract shape representations in terms of geometric volumes, the kind of shape representation that has been hypothesized to underlie mature objectrecognition. Further, these differences were strongly linked in individual children to the number of object names in their productive vocabulary. Experiment 2 replicated these results and showed further that the less advanced children’s objectrecognition was based on the piecemeal use of individual features and parts, rather than overall shape. The results provide further evidence for significant and rapid developmental changes in objectrecognition during the same period children first learn object names. The implications of the results for theories of visual objectrecognition, the relation of objectrecognition to category learning, and underlying developmental processes are discussed. PMID:19120414

In this study, the objectrecognition problem was solved using support plane method. The modelled SAR images were used as features vectors in the recognition algorithm. Radar signal backscattering of objects in different observing poses is presented in SAR images. For real time simulation, we used simple mixture model of Lambertian-specular reflectivity. To this end, an algorithm of ray tracing is extended for simulating SAR images of 3D man-made models. The suggested algorithm of support plane is very effective in objectsrecognition using SAR images and RCS diagrams.

Three-dimensional objectrecognition and reconstruction (ORR) is a research area of major interest in computer vision and photogrammetry. Virtual cities, for example, is one of the exciting application fields of ORR which became very popular during the last decade. Natural and man-made objects of cities such as trees and buildings are complex structures and automatic recognition and reconstruction of these objects from digital aerial images but also other data sources is a big challenge. In this paper a novel approach for objectrecognition is presented based on neuro-fuzzy modelling. Structural, textural and spectral information is extracted and integrated in a fuzzy reasoning process. The learning capability of neural networks is introduced to the fuzzy recognition process by taking adaptable parameter sets into account which leads to the neuro-fuzzy approach. Object reconstruction follows recognition seamlessly by using the recognition output and the descriptors which have been extracted for recognition. A first successful application of this new ORR approach is demonstrated for the three object classes 'buildings', 'cars' and 'trees' by using aerial colour images of an urban area of the town of Engen in Germany.

Object perception involves a range of visual and cognitive processes, and is known to include both a feedfoward flow of information from early visual cortical areas to higher cortical areas, along with feedback from areas such as prefrontal cortex. Previous studies have found that low and high spatial frequency information regarding object identity may be processed over different timescales. Here we used the high temporal resolution of magnetoencephalography (MEG) combined with multivariate pattern analysis to measure information specifically related to object identity in peri-frontal and peri-occipital areas. Using stimuli closely matched in their low-level visual content, we found that activity in peri-occipital cortex could be used to decode object identity from ~80ms post stimulus onset, and activity in peri-frontal cortex could also be used to decode object identity from a later time (~265ms post stimulus onset). Low spatial frequency information related to object identity was present in the MEG signal at an earlier time than high spatial frequency information for peri-occipital cortex, but not for peri-frontal cortex. We additionally used Granger causality analysis to compare feedforward and feedback influences on representational content, and found evidence of both an early feedfoward flow and later feedback flow of information related to object identity. We discuss our findings in relation to existing theories of object processing and propose how the methods we use here could be used to address further questions of the neural substrates underlying object perception. PMID:26806290

Recognition memory requires processing of various types of information such as objects and locations. Impairment in recognition memory is a prominent feature of amnesia and a symptom of Alzheimer’s disease (AD). Basal forebrain cholinergic neurons contain two major groups, one localized in the medial septum (MS)/vertical diagonal band of Broca (vDB), and the other in the nucleus basalis magnocellularis (NBM). The roles of these cell groups in recognition memory have been debated, and it remains unclear how they contribute to it. We use a genetic cell targeting technique to selectively eliminate cholinergic cell groups and then test spatial and objectrecognition memory through different behavioural tasks. Eliminating MS/vDB neurons impairs spatial but not objectrecognition memory in the reference and working memory tasks, whereas NBM elimination undermines only objectrecognition memory in the working memory task. These impairments are restored by treatment with acetylcholinesterase inhibitors, anti-dementia drugs for AD. Our results highlight that MS/vDB and NBM cholinergic neurons are not only implicated in recognition memory but also have essential roles in different types of recognition memory. PMID:26246157

This report is a general description of an automatic target recognitionsystem developed at the Idaho National Engineering Laboratory for the Department of Energy. A user`s manual is a separate volume, Automatic TLI RecognitionSystem, User`s Guide, and a programmer`s manual is Automatic TLI RecognitionSystem, Programmer`s Guide. This system was designed as an automatic target recognitionsystem for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system naturally incorporates image data fusion, and it gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. In addition to its primary function as a trainable target recognitionsystem, this is also a versatile, general-purpose tool for image manipulation and analysis, which can be either keyboard-driven or script-driven. This report includes descriptions of three variants of the computer hardware, a description of the mathematical basis if the training process, and a description with examples of the system capabilities.

Considerable work has been reported in recent years on the utilization of hierarchical architectures for efficient classification of image data typically encountered in task domains relevant to automated inspection, part sorting, quality monitoring, and so on. Such work has opened up the possibility of further enhancements through the more effective use of multiple-experts in such structures, but a principal difficulty encountered is to formulate an efficient way to combine decisions of individual experts to form a consensus. The approach proposed here can be envisaged as a structure with multiple layers of filters to separate an input object/image stream. In an n-way classification problem, the primary layer channels the input stream into n different streams, with subsequent further processing dependent on the form of decision taken at the earlier stages. The decision about combining the initially filtered streams is taken based on the degree of confusion among the classes present. The filter battery effectively creates two separate types of output. One is the relatively well-behaved filtered stream corresponding to the defined target classes, while the other contains the patterns which are rejected by different filters as not belonging to the target stream. Subsequently, more specialized classifiers are trained to recognize the intended target classes only, while the rejected patterns from all the second layer filters are collected and presented to a reject recovery classifier which is trained on all the n input classes. Thus, progressively more focusing of the decision making occurs as the processing path is traversed, with the resultant increase in the overall classification capability of the overall system. In this paper, classification results are presented to illustrate the relative performance levels achieved with single expert classifiers in comparison with this type of multi-expert configuration where these single experts are integrated within the

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved objectrecognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific objectrecognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception. PMID:21038986

In speaker recognitionsystems, one of the key feature parameters is MFCC, which can be used for speaker recognition. So, how to extract MFCC parameter in speech signals more exactly and efficiently, decides the performance of the system. Theoretically, MFCC parameters are used to describe the spectrum envelope of the vocal tract characteristics and often ignore the impacts of fundamental frequency. But in practice, MFCC can be influenced by fundamental frequency which can cause palpable performance reduction. So, smoothing MFCC (SMFCC), which based on smoothing short-term spectral amplitude envelope, has been proposed to improve MFCC algorithm. Experimental results show that improved MFCC parameters---SMFCC can degrade the bad influences of fundamental frequency effectively and upgrade the performances of speaker recognitionsystem. Especially for female speakers, who have higher fundamental frequency, the recognition rate improves more significantly.

A single radiology department serves the three separate organizations that comprise Emory Healthcare in Atlanta--three separate hospitals, the Emory Clinic and the Emory University School of Medicine. In 1996, the chairman of Emory Healthcare issued a mandate to the radiology department to decrease its report turnaround time, provide better service and increase customer satisfaction. The area where the greatest effect could be made without involving the transcription area was the "exam complete to dictate" piece of the reporting process. A committee investigating voice recognitionsystems established an essential criteria for potential vendors--to be able to download patient scheduling and demographic information from the existing RIS to the new system. Second, the system had to be flexible and straightforward for doctors to learn. It must have a word processing package for easy report correction and editing, and a microphone that would rewind and correct dictation before recognition took place. To keep capital costs low for the pilot, the committee opted for server recognition rather than purchase the expensive workstations necessary for real-time recognition. A switch was made later to real-time recognition. PACS and voice recognition have proven to be highly complementary. Most importantly, the new system has had a tremendous impact on turnaround time in the "dictate to final" phase. Once in the 30-hour range, 65 percent of the reports are now turned around in less than 15 minutes, 80 percent in less than 30 minutes, and 90 percent in less than an hour. PMID:10558032

The ability to quickly categorize visual scenes is critical to daily life, allowing us to identify our whereabouts and to navigate from one place to another. Rapid scene categorization relies heavily on the kinds of objects scenes contain; for instance, studies have shown that recognition is less accurate for scenes to which incongruent objects have been added, an effect usually interpreted as evidence of objects' general capacity to activate semantic networks for scene categories they are statistically associated with. Essentially all real-world scenes contain multiple objects, however, and it is unclear whether scene recognition draws on the scene associations of individual objects or of object groups. To test the hypothesis that scene recognition is steered, at least in part, by associations between object groups and scene categories, we asked observers to categorize briefly-viewed scenes appearing with object pairs that were semantically consistent or inconsistent with the scenes. In line with previous results, scenes were less accurately recognized when viewed with inconsistent versus consistent pairs. To understand whether this reflected individual or group-level object associations, we compared the impact of pairs composed of mutually related versus unrelated objects; i.e., pairs, which, as groups, had clear associations to particular scene categories versus those that did not. Although related and unrelated object pairs equally reduced scene recognition accuracy, unrelated pairs were consistently less capable of drawing erroneous scene judgments towards scene categories associated with their individual objects. This suggests that scene judgments were influenced by the scene associations of object groups, beyond the influence of individual objects. More generally, the fact that unrelated objects were as capable of degrading categorization accuracy as related objects, while less capable of generating specific alternative judgments, indicates that the process

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognitionsystems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognitionsystem. In this paper, we propose and formalize a performance evaluation model for the biometric recognitionsystem, implementing an evaluation tool for face recognitionsystems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process. PMID:18317524

Performance is often impaired linearly with increasing angular disparity between two objects in tasks that measure mental rotation or objectrecognition. But increased angular disparity is often accompanied by changes in the similarity between views of an object, confounding the impact of the two factors in these tasks. We examined separately the…

Cognitive neuroscientific research proposes complementary hemispheric asymmetries in naming and recognising visual objects, with a left temporal lobe advantage for object naming and a right temporal lobe advantage for objectrecognition. Specifically, it has been proposed that the left inferior temporal lobe plays a mediational role linking…

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

An optimal sensing strategy for an optical proximity sensor system engaged in the recognition and localization of 3-D natural quadric objects is presented. The optimal sensing strategy consists of the selection of an optimal beam orientation and the determination of an optimal probing plane that compose an optimal data collection operation known as an optimal probing. The decision of an optimal probing is based on the measure of discrimination power of a cluster of surfaces on a multiple interpretation image (MII), where the measure of discrimination power is defined in terms of a utility function computing the expected number of interpretations that can be pruned out by a probing. An object representation suitable for active sensing based on a surface description vector (SDV) distribution graph and hierarchical tables is presented. Experimental results are shown.

This article presents a method for the object classification that combines a generative template and a discriminative classifier. The method is a variant of the support vector machine (SVM), which uses Multiple Kernel Learning (MKL). The features are extracted from a generative template so called Active Basis template. Before using them for object classification, we construct a visual vocabulary by clustering a set of training features according to their orientations. To keep the spatial information, a "spatial pyramid" is used. The strength of this approach is that it combines the rich information encoded in the generative template, the Active Basis, with the discriminative power of the SVM algorithm. We show promising results of experiments for images from the LHI dataset.

Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published

A new application for VR has emerged: product development, in which several stakeholders (from engineers to end users) use the same VR for development and communicate purposes. Various characteristics among these stakeholders vary considerably, which imposes potential constraints to the VR. The current paper discusses the influence of three types of exploration of objects (i.e., none, passive, active) on one of these characteristics: the ability to form mental representations or visuo-spatial ability (VSA). Through an experiment we found that all users benefit from exploring objects. Moreover, people with low VSA (e.g., end users) benefit from an interactive exploration of objects opposed to people with a medium or high VSA (e.g. engineers), who are not sensitive for the type of exploration. Hence, for VR environments in which multiple stakeholders participate (e.g. for product development), differences among their cognitive abilities (e.g., VSA) have to be taken into account to enable an efficient usage of VR.

This report describes the beta prototype automatic target recognitionsystem ATR3, and some performance tests done with this system. This is a fully operational system, with a high computational speed. It is useful for findings any kind of target in digitized image data, and as a general purpose image analysis tool.

The U. S. Army Night Vision and Electronic Sensors Directorate (NVESD) recently tested an explosive-hazards detection vehicle that combines a pulsed FLGPR with a visible-spectrum color camera. Additionally, NVESD tested a human-in-the-loop multi-camera system with the same goal in mind. It contains wide field-of-view color and infrared cameras as well as zoomable narrow field-of-view versions of those modalities. Even though they are separate vehicles, having information from both systems offers great potential for information fusion. Based on previous work at the University of Missouri, we are not only able to register the UTM-based positions of the FLGPR to the color image sequences on the first system, but we can register these locations to corresponding image frames of all sensors on the human-in-the-loop platform. This paper presents our approach to first generate libraries of multi-sensor information across these platforms. Subsequently, research is performed in feature extraction and recognition algorithms based on the multi-sensor signatures. Our goal is to tailor specific algorithms to recognize and eliminate different categories of clutter and to be able to identify particular explosive hazards. We demonstrate our library creation, feature extraction and objectrecognition results on a large data collection at a US Army test site.

A speech recognitionsystem is described for an automotive vehicle for activating vehicle actuators in response to predetermined spoken instructions supplied to the system via a microphone, which comprises: (a) a manually controlled record switch for deriving a record signal when activated; (b) a manually controlled recognition switch for deriving a recognition signal when activated; (c) a speech recognizer for sequentially recording reference spoken instructions whenever one reference spoken instruction is supplied to the system through the microphone while the record switch is activated, a memory having a storage area for each spoken instruction, and means for shifting access to each storage area for each spoken instruction has been recorded in the storage area provided therefore. A means is included for activating vehicle actuators sequentially whenever one recognition spoken instruction is supplied to the system via the microphone while the recognition switch is activated and when the spoken instruction to be recognized is similar to the reference spoken instruction; and (d) means for deriving skip instruction signal and for coupling the skip instruction signal to the speech recognizer to shift access from a currently accessed storage area for recording a current reference spoken instruction to a succeeding storage area for recording a succeeding reference spoken instruction even when the current reference spoken instruction is not supplied to the system through the microphone.

This paper describes a method concerned with the design and construction of a systeL to measure and record discrete surface locations from actual physical objects. It also investigates different image processing techniques, compaction of graphical data algorithms and 3-D object reconstruction and manipulation, from a laser scanner for the reconstruction of the human soft tissue of the face out of the skull. It is a robotic laser ranging system that automatically generates three dimensional surface co-ordinates starting from homemorphic surfaced objects. The principle of the digitising process is based on triangulation between a laser point, illuminated on the surface of the object and two custom-built light sensors. Given the geometry of the system, one of the light sensors detects the small spot on the object and then calculates a representative point in Euclidean three space. A two degree-offreedom electro-mechanical system translates the laser and rotates the object in order to discretise the entire object. Representations of complex real objects have been generated in a relatively short time with very good resolution. For example, a human skull can be digitised, representing over 5000 surface points, in a little over one hour. The data representations can then be viewed and manipulated in real time on high performance graphics devices or viewed and then animated as a realistic image on raster graphics. The principal aim of this project is to develop Artificial Intelligence and Knowledge based system techniques to infer the depth of the soft tissue and its associated relationship with the skull.

The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class objectrecognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing objectrecognition. PMID:26906591

When a degraded two-tone image such as a “Mooney” image is seen for the first time, it is unrecognizable in the initial seconds. The recognition of such an image is facilitated by giving prior information on the object, which is known as top-down facilitation and has been intensively studied. Even in the absence of any prior information, however, we experience sudden perception of the emergence of a salient object after continued observation of the image, whose processes remain poorly understood. This emergent recognition is characterized by a comparatively long reaction time ranging from seconds to tens of seconds. In this study, to explore this time-consuming process of emergent recognition, we investigated the properties of the reaction times for recognition of degraded images of various objects. The results show that the time-consuming component of the reaction times follows a specific exponential function related to levels of image degradation and subject's capability. Because generally an exponential time is required for multiple stochastic events to co-occur, we constructed a descriptive mathematical model inspired by the neurophysiological idea of combination coding of visual objects. Our model assumed that the coincidence of stochastic events complement the information loss of a degraded image leading to the recognition of its hidden object, which could successfully explain the experimental results. Furthermore, to see whether the present results are specific to the task of emergent recognition, we also conducted a comparison experiment with the task of perceptual decision making of degraded images, which is well known to be modeled by the stochastic diffusion process. The results indicate that the exponential dependence on the level of image degradation is specific to emergent recognition. The present study suggests that emergent recognition is caused by the underlying stochastic process which is based on the coincidence of multiple stochastic events

Zero-Copy ObjectsSystem software enables application data to be encapsulated in layers of communication protocol without being copied. Indirect referencing enables application source data, either in memory or in a file, to be encapsulated in place within an unlimited number of protocol headers and/or trailers. Zero-copy objects (ZCOs) are abstract data access representations designed to minimize I/O (input/output) in the encapsulation of application source data within one or more layers of communication protocol structure. They are constructed within the heap space of a Simple Data Recorder (SDR) data store to which all participating layers of the stack must have access. Each ZCO contains general information enabling access to the core source data object (an item of application data), together with (a) a linked list of zero or more specific extents that reference portions of this source data object, and (b) linked lists of protocol header and trailer capsules. The concatenation of the headers (in ascending stack sequence), the source data object extents, and the trailers (in descending stack sequence) constitute the transmitted data object constructed from the ZCO. This scheme enables a source data object to be encapsulated in a succession of protocol layers without ever having to be copied from a buffer at one layer of the protocol stack to an encapsulating buffer at a lower layer of the stack. For large source data objects, the savings in copy time and reduction in memory consumption may be considerable.

Recent advances in medical imaging technology have dramatically increased the amount of clinical image data. In contrast, techniques for efficiently exploiting the rich semantic information in medical images have evolved much slower. Despite the research outcomes in image understanding, current image databases are still indexed by manually assigned subjective keywords instead of the semantics of the images. Indeed, most current content-based image search applications index image features that do not generalize well and use inflexible queries. This slow progress is due to the lack of scalable and generic information representation systems which can abstract over the high dimensional nature of medical images as well as semantically model the results of objectrecognition techniques. We propose a system combining medical imaging information with ontological formalized semantic knowledge that provides a basis for building universal knowledge repositories and gives clinicians fully cross-lingual and cross-modal access to biomedical information.

Genetically-modified mice without the dopamine transporter (DAT) are hyperdopaminergic, and serve as models for studies of addiction, mania and hyperactive disorders. Here we investigated the capacity for objectrecognition in mildly hyperdopaminergic mice heterozygous for DAT (DAT +/-), with synaptic dopaminergic levels situated between those shown by DAT -/- homozygous and wild-type (WT) mice. We used a classical dopamine D2 antagonist, haloperidol, to modulate the levels of dopaminergic transmission in a dose-dependent manner, before or after exploring novel objects. In comparison with WT mice, DAT +/- mice showed a deficit in objectrecognition upon subsequent testing 24h later. This deficit was compensated by a single 0.05mg/kg haloperidol injection 30min before training. In all mice, a 0.3mg/kg haloperidol injected immediately after training impaired objectrecognition. The results indicate that a mild enhancement of dopaminergic levels can be detrimental to objectrecognition, and that this deficit can be rescued by a low dose of a D2 dopamine receptor antagonist. This suggests that novel objectrecognition is optimal at intermediate levels of D2 receptor activity. PMID:27059337

Recognition of objects and their relations is necessary for orienting in real life. We examined cognitive processes related to recognition of objects, their relations, and the patterns they form by using the game of chess. Chess enables us to compare experts with novices and thus gain insight in the nature of development of recognition skills. Eye movement recordings showed that experts were generally faster than novices on a task that required enumeration of relations between chess objects because their extensive knowledge enabled them to immediately focus on the objects of interest. The advantage was less pronounced on random positions where the location of chess objects, and thus typical relations between them, was randomized. Neuroimaging data related experts' superior performance to the areas along the dorsal stream-bilateral posterior temporal areas and left inferior parietal lobe were related to recognition of object and their functions. The bilateral collateral sulci, together with bilateral retrosplenial cortex, were also more sensitive to normal than random positions among experts indicating their involvement in pattern recognition. The pattern of activations suggests experts engage the same regions as novices, but also that they employ novel additional regions. Expert processing, as the final stage of development, is qualitatively different than novice processing, which can be viewed as the starting stage. Since we are all experts in real life and dealing with meaningful stimuli in typical contexts, our results underline the importance of expert-like cognitive processing on generalization of laboratory results to everyday life. PMID:21998070

It is rate to find pieces of stone, wood, metal, or glass in food packets, but when they occur, these foreign objects (FOs) cause distress to the consumer and concern to the manufacturer. Using x-ray imaging to detect FOs within food bags, hard contaminants such as stone or metal appear darker, whereas soft contaminants such as wood or rubber appear slightly lighter than the food substrate. In this paper we concentrate on the detection of soft contaminants such as small pieces of wood in bags of frozen corn kernels. Convolution masks are used to generate textural features which are then classified into corresponding homogeneous regions on the image using an artificial neural network (ANN) classifier. The separate ANN outputs are combined using a majority operator, and region discrepancies are removed by a median filter. Comparisons with classical classifiers showed the ANN approach to have the best overall combination of characteristics for our particular problem. The detected boundaries are in good agreement with the visually perceived segmentations.

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method. PMID:25532210

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

Human listeners are capable of identifying a speaker, over the telephone or an entryway out of sight, by listening to the voice of the speaker. Achieving this intrinsic human specific capability is a major challenge for Voice Biometrics. Like human listeners, voice biometrics uses the features of a person's voice to ascertain the speaker's identity. The best-known commercialized forms of voice Biometrics is Speaker RecognitionSystem (SRS). Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. This literature survey paper gives brief introduction on SRS, and then discusses general architecture of SRS, biometric standards relevant to voice/speech, typical applications of SRS, and current research in Speaker RecognitionSystems. We have also surveyed various approaches for SRS.

Two important and related developments in children between 18 and 24 months of age are the rapid expansion of object name vocabularies and the emergence of an ability to recognize objects from sparse representations of their geometric shapes. In the same period, children also begin to show a preference for planar views (i.e., views of objects held…

This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective objectrecognition can make delineation most accurate.

Recent research indicates that culture penetrates fundamental processes of perception and cognition. Here, we provide evidence that these influences begin early and influence how preschool children recognize common objects. The three tasks (N=128) examined the degree to which nonface objectrecognition by 3-year-olds was based on individual diagnostic features versus more configural and holistic processing. Task 1 used a 6-alternative forced choice task in which children were asked to find a named category in arrays of masked objects where only three diagnostic features were visible for each object. U.S. children outperformed age-matched Japanese children. Task 2 presented pictures of objects to children piece by piece. U.S. children recognized the objects given fewer pieces than Japanese children, and the likelihood of recognition increased for U.S. children, but not Japanese children, when the piece added was rated by both U.S. and Japanese adults as highly defining. Task 3 used a standard measure of configural progressing, asking the degree to which recognition of matching pictures was disrupted by the rotation of one picture. Japanese children's recognition was more disrupted by inversion than was that of U.S. children, indicating more configural processing by Japanese than U.S. children. The pattern suggests early cross-cultural differences in visual processing; findings that raise important questions about how visual experiences differ across cultures and about universal patterns of cognitive development. PMID:26985576

Recognition of anatomical structures is an important step in model based medical image segmentation. It provides pose estimation of objects and information about "where" roughly the objects are in the image and distinguishing them from other object-like entities. In,1 we presented a general method of model-based multi-objectrecognition to assist in segmentation (delineation) tasks. It exploits the pose relationship that can be encoded, via the concept of ball scale (b-scale), between the binary training objects and their associated grey images. The goal was to place the model, in a single shot, close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. Unlike position and scale parameters, we observe that orientation parameters require more attention when estimating the pose of the model as even small differences in orientation parameters can lead to inappropriate recognition. Motivated from the non-Euclidean nature of the pose information, we propose in this paper the use of non-Euclidean metrics to estimate orientation of the anatomical structures for more accurate recognition and segmentation. We statistically analyze and evaluate the following metrics for orientation estimation: Euclidean, Log-Euclidean, Root-Euclidean, Procrustes Size-and-Shape, and mean Hermitian metrics. The results show that mean Hermitian and Cholesky decomposition metrics provide more accurate orientation estimates than other Euclidean and non-Euclidean metrics.

Spontaneous recognition of a novel object is a popular measure of exploratory behavior, perception and recognition memory in rodent models. Because of its relative simplicity and speed of testing, the variety of stimuli that can be used, and its ecological validity across species, it is also an attractive task for comparative research. To date, variants of this test have been used with vertebrate and invertebrate species, but the methods have seldom been sufficiently standardized to allow cross-species comparison. Here, we review the methods necessary for the study of novel objectrecognition in mammalian and non-mammalian models, as well as the results of these experiments. Critical to the use of this test is an understanding of the organism’s initial response to a novel object, the modulation of exploration by context, and species differences in object perception and exploratory behaviors. We argue that with appropriate consideration of species differences in perception, object affordances, and natural exploratory behaviors, the spontaneous objectrecognition test can be a valid and versatile tool for translational research with non-mammalian models. PMID:26217207

The application of deregulation policies in electric power systems has resulted in the necessity to quantify the quality of electric power. This fact highlights the need for a new monitoring strategy which is capable of tracking, detecting, classifying power quality disturbances, and then identifying the source of the disturbance. The objective of this work is to design an efficient and reliable power quality monitoring strategy that uses the advances in signal processing and pattern recognition to overcome the deficiencies that exist in power quality monitoring devices. The purposed monitoring strategy has two stages. The first stage is to detect, track, and classify any power quality violation by the use of on-line measurements. In the second stage, the source of the classified power quality disturbance must be identified. In the first stage, an adaptive linear combiner is used to detect power quality disturbances. Then, the Teager Energy Operator and Hilbert Transform are utilized for power quality event tracking. After the Fourier, Wavelet, and Walsh Transforms are employed for the feature extraction, two approaches are then exploited to classify the different power quality disturbances. The first approach depends on comparing the disturbance to be classified with a stored set of signatures for different power quality disturbances. The comparison is developed by using Hidden Markov Models and Dynamic Time Warping. The second approach depends on employing an inductive inference to generate the classification rules directly from the data. In the second stage of the new monitoring strategy, only the problem of identifying the location of the switched capacitor which initiates the transients is investigated. The Total Least Square-Estimation of Signal Parameters via Rotational Invariance Technique is adopted to estimate the amplitudes and frequencies of the various modes contained in the voltage signal measured at the facility entrance. After extracting the

Computational imaging (CI) systems are hybrid imagers in which the optical and post-processing sub-systems are jointly optimized to maximize the task-specific performance. In this dissertation we consider a form of CI system that measures the linear projections (i.e., features) of the scene optically, and it is commonly referred to as feature-specific imaging (FSI). Most of the previous work on FSI has been concerned with image reconstruction. Previous FSI techniques have also been non-adaptive and restricted to the use of ambient illumination. We consider two novel extensions of the FSI system in this work. We first present an adaptive feature-specific imaging (AFSI) system and consider its application to a face-recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. We present both statistical and information-theoretic adaptation mechanisms for the AFSI system. The sequential hypothesis testing framework is used to determine the number of measurements required for achieving a specified misclassification probability. We demonstrate that AFSI system requires significantly fewer measurements than static-FSI (SFSI) and conventional imaging at low signal-to-noise ratio (SNR). We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage. Experimental results validating the AFSI system are presented. Next we present a FSI system based on the use of structured light. Feature measurements are obtained by projecting spatially structured illumination onto an object and collecting all of the reflected light onto a single photodetector. We refer to this system as feature-specific structured imaging (FSSI). Principal component features are used to define the illumination patterns. The optimal LMMSE operator is used to generate object estimates from the measurements. We demonstrate that this new imaging approach reduces imager complexity and provides improved image

Efficient processing of our complex visual environment is essential and many daily visual tasks rely on accurate and fast objectrecognition. It is therefore important to evaluate how objectrecognition performance evolves during the course of adulthood. Surprisingly, this ability has not yet been investigated in the aged population, although several neuroimaging studies have reported altered activity in high-level visual ventral regions when elderly subjects process natural stimuli. In the present study, color photographs of various objects embedded in contextual scenes were used to assess object categorization performance in 97 participants aged from 20 to 91. Objects were either animals or pieces of furniture, embedded in either congruent or incongruent contexts. In every age group, subjects showed reduced categorization performance, both in terms of accuracy and speed, when objects were seen in incongruent vs. congruent contexts. In subjects over 60 years old, object categorization was greatly slowed down when compared to young and middle-aged subjects. Moreover, subjects over 75 years old evidenced a significant decrease in categorization accuracy when objects were seen in incongruent contexts. This indicates that incongruence of the scene may be particularly disturbing in late adulthood, therefore impairing objectrecognition. Our results suggest that daily visual processing of complex natural environments may be less efficient with age, which might impact performance in everyday visual tasks. PMID:23891714

Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

In this work we investigate the possibilities offered by a minimal framework of artificial spiking neurons to be deployed in silico. Here we introduce a hierarchical network architecture of spiking neurons which learns to recognize moving objects in a visual environment and determine the correct motor output for each object. These tasks are learned through both supervised and unsupervised spike timing dependent plasticity (STDP). STDP is responsible for the strengthening (or weakening) of synapses in relation to pre- and post-synaptic spike times and has been described as a Hebbian paradigm taking place both in vitro and in vivo. We utilize a variation of STDP learning, called burst-STDP, which is based on the notion that, since spikes are expensive in terms of energy consumption, then strong bursting activity carries more information than single (sparse) spikes. Furthermore, this learning algorithm takes advantage of homeostatic renormalization, which has been hypothesized to promote memory consolidation during NREM sleep. Using this learning rule, we design a spiking neural network architecture capable of objectrecognition, motion detection, attention towards important objects, and motor control outputs. We demonstrate the abilities of our design in a simple environment with distractor objects, multiple objects moving concurrently, and in the presence of noise. Most importantly, we show how this neural network is capable of performing these tasks using a simple leaky-integrate-and-fire (LIF) neuron model with binary synapses, making it fully compatible with state-of-the-art digital neuromorphic hardware designs. As such, the building blocks and learning rules presented in this paper appear promising for scalable fully neuromorphic systems to be implemented in hardware chips. PMID:22615855

Light interactions with matter is of remarkable complexity. An adequate modeling of global illumination is a vastly studied topic since the beginning of computer graphics, and still is an unsolved problem. The rendering equation for global illumination is based of refraction and reflection of light in interaction with matter within an environment. This physical process possesses a high computational complexity when implemented in a digital computer. The appearance of an object depends on light interactions with the surface of the material, such as emission, scattering, and absorption. Several image-synthesis methods have been used to realistically render the appearance of light incidence on an object. Recent global illumination algorithms employ mathematical models and computational strategies that improve the efficiency of the simulation solution. This work presents a review the state of the art of global illumination algorithms and focuses on the efficiency of the solution in a computational implementation in a graphics processing unit. A reliable system is developed to simulate realistics scenes in the context of real-time objectrecognition under different lighting conditions. Computer simulations results are presented and discussed in terms of discrimination capability, and robustness to additive noise, when considering several lighting model reflections and multiple light sources.

Environmental enrichment (EE) is an experimental paradigm in which rodents are housed in complex environments containing objects that provide stimulation, the effects of which are expected to improve the welfare of these subjects. EE has been shown to considerably improve learning and memory in rodents. However, knowledge about the effects of EE on social interaction is generally limited and rather controversial. Thus, our aim was to evaluate both novel objectrecognition and agonistic behavior in NMRI mice receiving EE, hypothesizing enhanced cognition and slightly enhanced agonistic interaction upon EE rearing. During a 4-week period half the mice (n = 16) were exposed to EE and the other half (n = 16) remained in a standard environment (SE). On PND 56-57, animals performed the objectrecognition test, in which recognition memory was measured using a discrimination index. The social interaction test consisted of an encounter between an experimental animal and a standard opponent. Results indicated that EE mice explored the new object for longer periods than SE animals (P < .05). During social encounters, EE mice devoted more time to sociability and agonistic behavior (P < .05) than their non-EE counterparts. In conclusion, EE has been shown to improve objectrecognition and increase agonistic behavior in adolescent/early adulthood mice. In the future we intend to extend this study on a longitudinal basis in order to assess in more depth the effect of EE and the consistency of the above-mentioned observations in NMRI mice. PMID:23588702

Analysis of data collected from behavioral paradigms has provided important information for understanding the etiology and progression of diseases that involve neural regions mediating abnormal behavior. The trace eyeblink conditioning (EBC) paradigm is particularly suited to examine cerebro-cerebellar interactions since the paradigm requires the cerebellum, forebrain, and awareness of the stimulus contingencies. Impairments in acquiring EBC have been noted in several neuropsychiatric conditions, including schizophrenia, Alzheimer’s disease (AD), progressive supranuclear palsy, and post-traumatic stress disorder. Although several species have been used to examine EBC, the rabbit is unique in its tolerance for restraint, which facilitates imaging, its relatively large skull that facilitates chronic neuronal recordings, a genetic sequence for amyloid that is identical to humans which makes it a valuable model to study AD, and in contrast to rodents, it has a striatum that is differentiated into a caudate and a putamen that facilitates analysis of diseases involving the striatum. This review focuses on EBC during schizophrenia and AD since impairments in cerebro-cerebellar connections have been hypothesized to lead to a cognitive dysmetria. We also relate EBC to conditioned avoidance responses that are more often examined for effects of antipsychotic medications, and we propose that an analysis of novel objectrecognition (NOR) may add to our understanding of how the underlying neural circuitry has changed during disease states. We propose that the EBC and NOR paradigms will help to determine which therapeutics are effective for treating the cognitive aspects of schizophrenia and AD, and that neuroimaging may reveal biomarkers of the diseases and help to evaluate potential therapeutics. The rabbit, thus, provides an important translational system for studying neural mechanisms mediating maladaptive behaviors that underlie some psychiatric diseases, especially

An augmented reality (AR) smartglass display combines real-world scenes with digital information enabling the rapid growth of AR-based applications. We present an augmented reality-based approach for three-dimensional (3D) optical visualization and objectrecognition using axially distributed sensing (ADS). For objectrecognition, the 3D scene is reconstructed, and feature extraction is performed by calculating the histogram of oriented gradients (HOG) of a sliding window. A support vector machine (SVM) is then used for classification. Once an object has been identified, the 3D reconstructed scene with the detected object is optically displayed in the smartglasses allowing the user to see the object, remove partial occlusions of the object, and provide critical information about the object such as 3D coordinates, which are not possible with conventional AR devices. To the best of our knowledge, this is the first report on combining axially distributed sensing with 3D object visualization and recognition for applications to augmented reality. The proposed approach can have benefits for many applications, including medical, military, transportation, and manufacturing. PMID:26766698

Feature extraction transforms an object`s image representation to an alternate reduced representation. In one-class objectrecognition, we would like this alternate representation to give improved discrimination between the object and all possible non-objects and improved generation between different object poses. Feature selection can be time-consuming and difficult to optimize so we have investigated unsupervised neural networks for feature discovery. We first discuss an inherent limitation in competitive type neural networks for discovering features in gray level images. We then show how Sanger`s Generalized Hebbian Algorithm (GHA) removes this limitation and describe a novel GHA application for learning object features that discriminate the object from clutter. Using a specific example, we show how these features are better at distinguishing the target object from other nontarget object with Carpenter`s ART 2-A as the pattern classifier.

It is believed that certain contour attributes, specifically orientation, curvature and linear extent, provide essential cues for object (shape) recognition. The present experiment examined this hypothesis by comparing stimulus conditions that differentially provided such cues. A spaced array of dots was used to mark the outside boundary of namable objects, and subsets were chosen that contained either contiguous strings of dots or randomly positioned dots. These subsets were briefly and successively displayed using an MTDC information persistence paradigm. Across the major range of temporal separation of the subsets, it was found that contiguity of boundary dots did not provide more effective shape recognition cues. This is at odds with the concept that encoding and recognition of shapes is predicated on the encoding of contour attributes such as orientation, curvature and linear extent. PMID:18593469

We aim at improving the objectrecognition with few training data in the target domain by leveraging abundant auxiliary data in the source domain. The major issue obstructing knowledge transfer from source to target is the limited correlation between the two domains. Transferring irrelevant information from the source domain usually leads to performance degradation in the target domain. To address this issue, we propose a transfer learning framework with the two key components, such as discriminative source data reconstruction and dual-domain boosting. The former correlates the two domains via reconstructing source data by target data in a discriminative manner. The latter discovers and delivers only knowledge shared by the target data and the reconstructed source data. Hence, it facilitates recognition in the target. The promising experimental results on three benchmarks of objectrecognition demonstrate the effectiveness of our approach. PMID:27244741

A novel two-stage protection scheme for automatic iris recognitionsystems against masquerade attacks carried out with synthetically reconstructed iris images is presented. The method uses different characteristics of real iris images to differentiate them from the synthetic ones, thereby addressing important security flaws detected in state-of-the-art commercial systems. Experiments are carried out on the publicly available Biosecure Database and demonstrate the efficacy of the proposed security enhancing approach.

Neural networks have been applied in numerous fields, including transformation invariant objectrecognition, wherein an object is recognized despite changes in the object's position in the input field, size, or rotation. One of the more successful neural network methods used in invariant objectrecognition is the higher-order neural network (HONN) method. With a HONN, known relationships are exploited and the desired invariances are built directly into the architecture of the network, eliminating the need for the network to learn invariance to transformations. This results in a significant reduction in the training time required, since the network needs to be trained on only one view of each object, not on numerous transformed views. Moreover, one hundred percent accuracy is guaranteed for images characterized by the built-in distortions, providing noise is not introduced through pixelation. The program HONTIOR implements a third-order neural network having invariance to translation, scale, and in-plane rotation built directly into the architecture, Thus, for 2-D transformation invariance, the network needs only to be trained on just one view of each object. HONTIOR can also be used for 3-D transformation invariant objectrecognition by training the network only on a set of out-of-plane rotated views. Historically, the major drawback of HONNs has been that the size of the input field was limited to the memory required for the large number of interconnections in a fully connected network. HONTIOR solves this problem by coarse coding the input images (coding an image as a set of overlapping but offset coarser images). Using this scheme, large input fields (4096 x 4096 pixels) can easily be represented using very little virtual memory (30Mb). The HONTIOR distribution consists of three main programs. The first program contains the training and testing routines for a third-order neural network. The second program contains the same training and testing procedures as the

Exposure to HZE particles produces changes in neurocognitive performance. These changes, including deficits in spatial learning and memory, objectrecognition memory and operant responding, are also observed in the aged organism. As such, it has been proposed that exposure to heavy particles produces "accelerated aging". Because aging is an ongoing process, it is possible that there would be an interaction between the effects of exposure and the effects of aging, such that doses of HZE particles that do not affect the performance of younger organisms will affect the performance of organisms as they age. The present experiments were designed to test the hypothesis that young rats that had been exposed to HZE particles would show a progressive deterioration in objectrecognition memory as a function of the age of testing. Rats were exposed to 12 C, 28 S or 48 Ti particles at the N.A.S.A. Space Radiation Laboratory at Brookhaven National Laboratory. Following irradiation the rats were shipped to UMBC for behavioral testing. HZE particle-induced changes in objectrecognition memory were tested using a standard procedure: rats were placed in an open field and allowed to interact with two identical objects for up to 30 sec; twenty-four hrs later the rats were again placed in the open field, this time containing one familiar and one novel object. Non-irradiated control animals spent significantly more time with the novel object than with the familiar object. In contrast, the rats that been exposed to heavy particles spent equal amounts of time with both the novel and familiar object. The lowest dose of HZE particles which produced a disruption of objectrecognition memory was determined three months and eleven months following exposure. The threshold dose needed to disrupt objectrecognition memory three months following irradiation varied as a function of the specific particle and energy. When tested eleven months following irradiation, doses of HZE particles that did

There is a bright future in the development and utilization of nanoscale systems based on intelligent materials that can respond to external input providing a beneficial function. Specific functional groups can be incorporated into polymers to make them responsive to environmental stimuli such as pH, temperature, or varying concentrations of biomolecules. The fusion of such “intelligent” biomaterials with nanotechnology has led to the development of powerful therapeutic and diagnostic platforms. For example, targeted release of proteins and chemotherapeutic drugs has been achieved using pH-responsive nanocarriers while biosensors with ultra-trace detection limits are being made using nanoscale, molecularly imprinted polymers. The efficacy of therapeutics and the sensitivity of diagnostic platforms will continue to progress as unique combinations of responsive polymers and nanomaterials emerge. PMID:24860724

This report describes how to use an automatic target recognitionsystem (version 14). In separate volumes are a general description of the ATR system, Automatic TLI RecognitionSystem, General Description, and a programmer`s manual, Automatic TLI RecognitionSystem, Programmer`s Guide.

This paper deals with the development of algorithms and software for optical recognition of growing defects in the semiconductor crystals and metal nanoparticles in colloidal solutions. Input information is a set of photographs from a microscope, as well as a short video-file with nanoparticle's tracks. We used the wavelet technology to filtering and image transformations. As a result of recognition the 3D image is formed with the point, linear and planar growing defects. Defects are sorted by size; different statistical characteristics are computed such as the defect's distribution in layers and in the whole crystal. The system supports arbitrary rotations of the "crystal"; "cutting" by different planes and so on. The software allows you to track the movement of nanoparticles in colloidal solutions; to determine the local temperature and density of the solution. We proposed a new method for quantitative estimation of recognition quality. This method based on the "virtual crystal" model, which has predetermined parameters of the defect subsystem. The software generates a set of photographs, which used as the input information of recognitionsystem. Comparing the statistical parameters of the input data with the recognition results, we can estimate the quality of recognitionsystems from different manufacturers.

The perirhinal cortex (PRh) has a well-established role in objectrecognition memory. More recent studies suggest that PRh is also important for two-choice visual discrimination tasks. Specifically, it has been suggested that PRh contains conjunctive representations that help resolve feature ambiguity, which occurs when a task cannot easily be…

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Three experiments assessed the development of children's part and configural (part-relational) processing in objectrecognition during adolescence. In total, 312 school children aged 7-16 years and 80 adults were tested in 3-alternative forced choice (3-AFC) tasks. They judged the correct appearance of upright and inverted presented familiar…

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognitionsystem for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognitionsystems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognitionsystem for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognitionsystems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

Non-negative matrix factorization of an input data matrix into a matrix of basis vectors and a matrix of encoding coefficients is a subspace representation method that has attracted attention of researches in pattern recognition in the recent period. We have explored crucial aspects of NMF on massive recognition experiments with the ORL database of faces which include intuitively clear parts constituting the whole. Using a principal changing of the learning stage structure and by formulating NMF problems for each of a priori given parts separately, we developed a novel modular NMF algorithm. Although this algorithm provides uniquely separated basis vectors which code individual face parts in accordance with the parts-based principle of the NMF methodology applied to objectrecognition problems, any significant improvement of recognition rates for occluded parts, predicted in several papers, was not reached. We claim that using the parts-based concept in NMF as a basis for solving recognition problems with occluded objects has not been justified.

This review explores the effects of female reproductive hormones, estrogens and progestogens, with a focus on progesterone and allopregnanolone, on object memory. Progesterone and its metabolites, in particular allopregnanolone, exert various effects on both cognitive and non-mnemonic functions in females. The well-known objectrecognition task is a valuable experimental paradigm that can be used to determine the effects and mechanisms of progestogens for mnemonic effects across the lifespan, which will be discussed herein. In this task there is little test-decay when different objects are used as targets and baseline valance for objects is controlled. This allows repeated testing, within-subjects designs, and longitudinal assessments, which aid understanding of changes in hormonal milieu. Objects are not aversive or food-based, which are hormone-sensitive factors. This review focuses on published data from our laboratory, and others, using the objectrecognition task in rodents to assess the role and mechanisms of progestogens throughout the lifespan. Improvements in objectrecognition performance of rodents are often associated with higher hormone levels in the hippocampus and prefrontal cortex during natural cycles, with hormone replacement following ovariectomy in young animals, or with aging. The capacity for reversal of age- and reproductive senescence-related decline in cognitive performance, and changes in neural plasticity that may be dissociated from peripheral effects with such decline, are discussed. The focus here will be on the effects of brain-derived factors, such as the neurosteroid, allopregnanolone, and other hormones, for enhancing objectrecognition across the lifespan. PMID:26235328

Any verification measurement performed on potentially classified nuclear material must satisfy two constraints. First and foremost, no classified information can be released to the monitoring party. At the same time, the monitoring party must gain sufficient confidence from the measurement to believe that the material being measured is consistent with the host's declarations concerning that material. The attribute measurement technique addresses both concerns by measuring several attributes of the nuclear material and displaying unclassified results through green (indicating that the material does possess the specified attribute) and red (indicating that the material does not possess the specified attribute) lights. The AVNG that we describe is an attribute measurement system built by RFNC-VNIIEF in Sarov, Russia. The AVNG measures the three attributes of 'plutonium presence,' 'plutonium mass >2 kg,' and 'plutonium isotopic ratio ({sup 240}Pu to {sup 239}Pu) <0.1' and was demonstrated in Sarov for a joint US/Russian audience in June 2009. In this presentation, we will outline the goals and objectives of the AVNG measurement system. These goals are driven by the two, sometimes conflicting, requirements mentioned above. We will describe the conceptual design of the AVNG and show how this conceptual design grew out of these goals and objectives.

Previous studies on the N-phenylpiperazine derivative LASSBio-579 have suggested that LASSBio-579 has an atypical antipsychotic profile. It binds to D2, D4 and 5-HT1A receptors and is effective in animal models of schizophrenia symptoms (prepulse inhibition disruption, apomorphine-induced climbing and amphetamine-induced stereotypy). In the current study, we evaluated the effect of LASSBio-579, clozapine (atypical antipsychotic) and haloperidol (typical antipsychotic) in the novel objectrecognition task, a recognition memory model with translational value. Haloperidol (0.01 mg/kg, orally) impaired the ability of the animals (CF1 mice) to recognize the novel object on short-term and long-term memory tasks, whereas LASSBio-579 (5 mg/kg, orally) and clozapine (1 mg/kg, orally) did not. In another set of experiments, animals previously treated with ketamine (10 mg/kg, intraperitoneally) or vehicle (saline 1 ml/100 g, intraperitoneally) received LASSBio-579, clozapine or haloperidol at different time-points: 1 h before training (encoding/consolidation); immediately after training (consolidation); or 1 h before long-term memory testing (retrieval). LASSBio-579 and clozapine protected against the long-term memory impairment induced by ketamine when administered at the stages of encoding, consolidation and retrieval of memory. These findings point to the potential of LASSBio-579 for treating cognitive symptoms of schizophrenia and other disorders. PMID:26513177

This work examined predictions of the interpolation of familiar views (IFV) account of objectrecognition performance in 5-month-olds. Infants were familiarized to an object either from a single viewpoint or from multiple viewpoints varying in rotation around a single axis. Objectrecognition was then tested in both conditions with the same object…

Preferential exploration of novel locations and objects by rodents has been used to test the effects of various manipulations on objectrecognition memory. However, manual scoring is time-consuming, requires extensive training, and is subject to inter-observer variability. Since rodents explore primarily by sniffing, we assessed the ability of new nose-point video tracking software (NPVT) to automatically collect objectrecognition data. Mice performed a novel object/novel location task and data collected by NPVT, two expert observers, and one inexperienced observer were compared. Percent time spent exploring the objects were correlated between the two expert observers and between NPVT and the two expert observers. In contrast, the inexperienced observer showed no correlation with either expert observer or NPVT. NPVT collected more reliable data compared to the inexperienced observer. NPVT and the expert observers gave similar group averages for arbitrarily assigned groups of mice, whereas the analysis of the inexperienced observer gave different results. Finally, NPVT generated valid results in a NO/NL experiment comparing mice expressing human apolipoprotein E3 versus E4, a risk factor for age-related cognitive decline. Video tracking with nose-point detection generates useful analyses of rodent objectrecognition task performance and possibly for other behavioral tests. PMID:18096240

An account is given of an image-processing system based on AI concepts, which allows input images produced by the CCT/Transit Instrument to be compared with a standard-object hierarchylike network of prototypes presented within the computer as 'frames'. Each frame contains information concerning either a standard object or the links among such objects. This method, by comparison to conventional, statistically-based pattern recognitionsystems, classifies data as an astronomer would and thereby lends credibility to its conclusions; it also furnishes a natural avenue for the machine's serendipitous discovery of new classes of objects.

Invariant recognition and motion tracking of 3-D objects under partial object viewing are difficult tasks. In this paper, we introduce a new neural network solution that is robust to noise corruption and partial viewing of objects. This method directly utilizes the acquired range data and requires no feature extraction. In the proposed approach, the object is first parametrically represented by a continuous distance transformation neural network (CDTNN) which is trained by the surface points of the exemplar object. When later presented with the surface points of an unknown object, this parametric representation allows the mismatch information to back-propagate through the CDTNN to gradually determine the best similarity transformation (translation and rotation) of the unknown object. The mismatch can be directly measured in the reconstructed representation domain between the model and the unknown object.

Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant objectrecognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations. PMID:27601096

Security solutions with the purpose to detect hidden objects underneath the clothing of persons are desired in many environments. With the variety of application scenarios criteria like flexibility and mobility become more important. So, many developments trend to focus on cameras, which can image scenes from a distance. This new generation of tools will have the advantage of hidden operation, which is believed by experts to add to the security because of its unpredictability. Such stand-off cameras do have some divergent requirements compared to mm-wave portal scanners. They will benefit from shorter wavelengths because of the higher optical resolution. In contrast to that, the needed transmission properties might become impractical at higher frequencies. A commonly accepted compromise is the use of wavelengths around 0.5mm. However, for stand-off cameras without oversized optical apertures, a resolution around 1cm is a practical limit. For our security camera "Safe VISITOR" (Safe VISible, Infrared and Terhaertz Objectrecognition) we have chosen to combine images from three different camera modules: a CCD for visible light, a microbolometer for long infrared (14μm) and a superconducting bolometer for 870μm. This combines the highest optical resolution (visible), the unprecedented temperature resolution at infrared and the almost perfect transmission at terahertz. We have built a first prototype and tested it in a field trial. We will present experimental results and try to assess the false error rate of our system.

Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant objectrecognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations. PMID:27601096

Retinal prostheses have the potential to restore partial vision. Objectrecognition in scenes of daily life is one of the essential tasks for implant wearers. Still limited by the low-resolution visual percepts provided by retinal prostheses, it is important to investigate and apply image processing methods to convey more useful visual information to the wearers. We proposed two image processing strategies based on Itti's visual saliency map, region of interest (ROI) extraction, and image segmentation. Itti's saliency model generated a saliency map from the original image, in which salient regions were grouped into ROI by the fuzzy c-means clustering. Then Grabcut generated a proto-object from the ROI labeled image which was recombined with background and enhanced in two ways--8-4 separated pixelization (8-4 SP) and background edge extraction (BEE). Results showed that both 8-4 SP and BEE had significantly higher recognition accuracy in comparison with direct pixelization (DP). Each saliency-based image processing strategy was subject to the performance of image segmentation. Under good and perfect segmentation conditions, BEE and 8-4 SP obtained noticeably higher recognition accuracy than DP, and under bad segmentation condition, only BEE boosted the performance. The application of saliency-based image processing strategies was verified to be beneficial to objectrecognition in daily scenes under simulated prosthetic vision. They are hoped to help the development of the image processing module for future retinal prostheses, and thus provide more benefit for the patients. PMID:25981202

While the application of local cortical cooling has recently become a focus of neurological research, extended localized deactivation deep within brain structures is still unexplored. Using a wirelessly controlled thermoelectric (Peltier) device and water-based heat sink, we have achieved inactivating temperatures (<20 C) at greater depths (>8 mm) than previously reported. After implanting the device into Long Evans rats' basolateral amygdala (BLA), an inhibitory brain center that controls anxiety and fear, we ran an open field test during which anxiety-driven behavioral tendencies were observed to decrease during cooling, thus confirming the device's effect on behavior. Our device will next be implanted in the rats' temporal association cortex (TeA) and recordings from our signal-tracing multichannel microelectrodes will measure and compare activated and deactivated neuronal activity so as to isolate and study the TeA signals responsible for objectrecognition. Having already achieved a top performing computational face-recognitionsystem, the lab will utilize this TeA activity data to generalize its computational efforts of face recognition to achieve general objectrecognition.

Real-world expertise provides a valuable opportunity to understand how experience shapes human behavior and neural function. In the visual domain, the study of expert objectrecognition, such as in car enthusiasts or bird watchers, has produced a large, growing, and often-controversial literature. Here, we synthesize this literature, focusing primarily on results from functional brain imaging, and propose an interactive framework that incorporates the impact of high-level factors, such as attention and conceptual knowledge, in supporting expertise. This framework contrasts with the perceptual view of object expertise that has concentrated largely on stimulus-driven processing in visual cortex. One prominent version of this perceptual account has almost exclusively focused on the relation of expertise to face processing and, in terms of the neural substrates, has centered on face-selective cortical regions such as the Fusiform Face Area (FFA). We discuss the limitations of this face-centric approach as well as the more general perceptual view, and highlight that expert related activity is: (i) found throughout visual cortex, not just FFA, with a strong relationship between neural response and behavioral expertise even in the earliest stages of visual processing, (ii) found outside visual cortex in areas such as parietal and prefrontal cortices, and (iii) modulated by the attentional engagement of the observer suggesting that it is neither automatic nor driven solely by stimulus properties. These findings strongly support a framework in which object expertise emerges from extensive interactions within and between the visual system and other cognitive systems, resulting in widespread, distributed patterns of expertise-related activity across the entire cortex. PMID:24409134

Real-world expertise provides a valuable opportunity to understand how experience shapes human behavior and neural function. In the visual domain, the study of expert objectrecognition, such as in car enthusiasts or bird watchers, has produced a large, growing, and often-controversial literature. Here, we synthesize this literature, focusing primarily on results from functional brain imaging, and propose an interactive framework that incorporates the impact of high-level factors, such as attention and conceptual knowledge, in supporting expertise. This framework contrasts with the perceptual view of object expertise that has concentrated largely on stimulus-driven processing in visual cortex. One prominent version of this perceptual account has almost exclusively focused on the relation of expertise to face processing and, in terms of the neural substrates, has centered on face-selective cortical regions such as the Fusiform Face Area (FFA). We discuss the limitations of this face-centric approach as well as the more general perceptual view, and highlight that expert related activity is: (i) found throughout visual cortex, not just FFA, with a strong relationship between neural response and behavioral expertise even in the earliest stages of visual processing, (ii) found outside visual cortex in areas such as parietal and prefrontal cortices, and (iii) modulated by the attentional engagement of the observer suggesting that it is neither automatic nor driven solely by stimulus properties. These findings strongly support a framework in which object expertise emerges from extensive interactions within and between the visual system and other cognitive systems, resulting in widespread, distributed patterns of expertise-related activity across the entire cortex. PMID:24409134

We report an extension of the procedure devised by Weinstein and Shanks (Memory & Cognition 36:1415-1428, 2008) to study false recognition and priming of pictures. Participants viewed scenes with multiple embedded objects (seen items), then studied the names of these objects and the names of other objects (read items). Finally, participants completed a combined direct (recognition) and indirect (identification) memory test that included seen items, read items, and new items. In the direct test, participants recognized pictures of seen and read items more often than new pictures. In the indirect test, participants' speed at identifying those same pictures was improved for pictures that they had actually studied, and also for falsely recognized pictures whose names they had read. These data provide new evidence that a false-memory induction procedure can elicit memory-like representations that are difficult to distinguish from "true" memories of studied pictures. PMID:22976882

Multiple-degree-of-freedom objectrecognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.

Crowding, the inability to recognize objects in clutter, sets a fundamental limit on conscious visual perception and objectrecognition throughout most of the visual field. Despite how widespread and essential it is to objectrecognition, reading, and visually guided action, a solid operational definition of what crowding is has only recently become clear. The goal of this review is to provide a broad-based synthesis of the most recent findings in this area, to define what crowding is and is not, and to set the stage for future work that will extend crowding well beyond low-level vision. Here we define five diagnostic criteria for what counts as crowding, and further describe factors that both escape and break crowding. All of these lead to the conclusion that crowding occurs at multiple stages in the visual hierarchy. PMID:21420894

The human hippocampus receives distinct signals via the lateral entorhinal cortex, typically associated with object features, and the medial entorhinal cortex, associated with spatial or contextual information. The existence of these distinct types of information calls for some means by which they can be managed in an appropriate way, by integrating them or keeping them separate as required to improve recognition. We hypothesize that several anatomical features of the hippocampus, including differentiation in connectivity between the superior/inferior blades of DG and the distal/proximal regions of CA3 and CA1, work together to play this information managing role. We construct a set of neural network models with these features and compare their recognition performance when given noisy or partial versions of contexts and their associated objects. We found that the anterior and posterior regions of the hippocampus naturally require different ratios of object and context input for optimal performance, due to the greater number of objects versus contexts. Additionally, we found that having separate processing regions in DG significantly aided recognition in situations where object inputs were degraded. However, split processing in both DG and CA3 resulted in performance tradeoffs, though the actual hippocampus may have ways of mitigating such losses. PMID:23781237

Recently advanced computational theories of 3D shape representation for recognition have focused on the alternative of viewer-centered vs. object-centered representation. Both approaches rely on establishing a correspondence between image data and the prototypical knowledge of object shape. This paper discusses the mathematical structures needed for organizing prototypical knowledge of object shape in a way that naturally relies to perceptual categories and thus allows for a flexible and efficient recognition process. The representational schema consists of a configuration of boundary based constituent parts which build the reference frame for qualitative and quantitative shape attributes. The decomposition into constituent parts maximizes convexity regions of the bounding surface and relies on extending the local classification into elliptic, hyperbolic, plane and parabolic to globally convex and nonconvex surface regions. The surface type of the parts guides and is preserved in a subsequent part approximation through generalized cones as volumetric primitives. This approach allows for a consistent characterization of surface and volumetric properties of object shape. A secondary segmentation into sub-parts and associated features is defined by the surface type and the type of change in cross section area along the axis. The two segmentation levels allows for a detailed and elaborate shape description. We show examples of shape description and discuss the representation in relation to the viewer-centered and object centered approaches to recognition.

The human hippocampus receives distinct signals via the lateral entorhinal cortex, typically associated with object features, and the medial entorhinal cortex, associated with spatial or contextual information. The existence of these distinct types of information calls for some means by which they can be managed in an appropriate way, by integrating them or keeping them separate as required to improve recognition. We hypothesize that several anatomical features of the hippocampus, including differentiation in connectivity between the superior/inferior blades of DG and the distal/proximal regions of CA3 and CA1, work together to play this information managing role. We construct a set of neural network models with these features and compare their recognition performance when given noisy or partial versions of contexts and their associated objects. We found that the anterior and posterior regions of the hippocampus naturally require different ratios of object and context input for optimal performance, due to the greater number of objects versus contexts. Additionally, we found that having separate processing regions in DG significantly aided recognition in situations where object inputs were degraded. However, split processing in both DG and CA3 resulted in performance tradeoffs, though the actual hippocampus may have ways of mitigating such losses. PMID:23781237

Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as 'beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as 'coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant objectrecognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for objectrecognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant objectrecognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant objectrecognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to

As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at 10-3 false accept rate. The latest results of our work can be found at http://www.cbl.uh.edu/UR8D/. PMID:17170476

A growing body of evidence suggests that the agglomeration of amyloid-β (Aβ) may be a trigger for Alzheimer׳s disease (AD). Central infusion of Aβ42 can lead to memory impairment in mice. Inhibiting the aggregation of Aβ has been considered a therapeutic strategy for AD. Endomorphin-1 (EM-1), an endogenous agonist of μ-opioid receptors, has been shown to inhibit the aggregation of Aβ in vitro. In the present study, we investigated whether EM-1 could alleviate the memory-impairing effects of Aβ42 in mice using novel objectrecognition (NOR) and object location recognition (OLR) tasks. We showed that co-administration of EM-1 was able to ameliorate Aβ42-induced amnesia in the lateral ventricle and the hippocampus, and these effects could not be inhibited by naloxone, an antagonist of μ-opioid receptors. Infusion of EM-1 or naloxone separately into the lateral ventricle had no influence on memory in the tasks. These results suggested that EM-1 might be effective as a drug for AD preventative treatment by inhibiting Aβ aggregation directly as a molecular modifier. PMID:26505914

Five experiments were conducted to determine whether primitive perceptual features, or textons, which Julesz (1984) identified in studies of texture segregation with adults, also affect objectrecognition early in development. Three-month-old infants discriminated Ts and Ls composed of overlapping line segments from +s but not from each other in a delayed-recognition test after 24 hr; however, Ts and Ls were discriminated from each other after only 1 hr. In a priming paradigm, Ts, Ls, and +s were discriminated from one another after 2 weeks. In succeeding experiments, infants exhibited adultlike visual pop-out effects in both delayed recognition and priming paradigms, detecting an L in the midst of 6 +s and vice versa; these effects were symmetrical. The pop-out effects apparently resulted from parallel search: Infants failed to detect 3 Ls among 4 +s. Clearly, some of the same primitive units that have been identified as the building blocks of adult visual perception underlie objectrecognition early in infancy. PMID:1431738

Investigating learning mechanisms in infancy relies largely on behavioural measures like visual attention, which often fail to predict whether stimuli would be encoded successfully. This study explored EEG activity in the theta frequency band, previously shown to predict successful learning in adults, to directly study infants' cognitive engagement, beyond visual attention. We tested 11-month-old infants (N = 23) and demonstrated that differences in frontal theta-band oscillations, recorded during infants' object exploration, predicted differential subsequent recognition of these objects in a preferential-looking test. Given that theta activity is modulated by motivation to learn in adults, these findings set the ground for future investigation into the drivers of infant learning. PMID:26018832

Investigating learning mechanisms in infancy relies largely on behavioural measures like visual attention, which often fail to predict whether stimuli would be encoded successfully. This study explored EEG activity in the theta frequency band, previously shown to predict successful learning in adults, to directly study infants' cognitive engagement, beyond visual attention. We tested 11-month-old infants (N = 23) and demonstrated that differences in frontal theta-band oscillations, recorded during infants' object exploration, predicted differential subsequent recognition of these objects in a preferential-looking test. Given that theta activity is modulated by motivation to learn in adults, these findings set the ground for future investigation into the drivers of infant learning. PMID:26018832

Objectrecognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the objectrecognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D objectrecognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matching.

In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old-new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/artificial speeded conceptual classification task with repeated attended, repeated unattended, and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classify them as natural or artificial. After the encoding phase, they performed an old-new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults. PMID:25628588

In the present study, we investigated the effects of selective attention at encoding on conceptual object priming (Experiment 1) and old–new recognition memory (Experiment 2) tasks in young and older adults. The procedures of both experiments included encoding and memory test phases separated by a short delay. At encoding, the picture outlines of two familiar objects, one in blue and the other in green, were presented to the left and to the right of fixation. In Experiment 1, participants were instructed to attend to the picture outline of a certain color and to classify the object as natural or artificial. After a short delay, participants performed a natural/artificial speeded conceptual classification task with repeated attended, repeated unattended, and new pictures. In Experiment 2, participants at encoding memorized the attended pictures and classify them as natural or artificial. After the encoding phase, they performed an old–new recognition memory task. Consistent with previous findings with perceptual priming tasks, we found that conceptual object priming, like explicit memory, required attention at encoding. Significant priming was obtained in both age groups, but only for those pictures that were attended at encoding. Although older adults were slower than young adults, both groups showed facilitation for attended pictures. In line with previous studies, young adults had better recognition memory than older adults. PMID:25628588

Diazinon is an organophosphate pesticide that is still heavily used in agriculture, home gardening, and indoor pest control in Japan. The present study investigated the effect of neonatal exposure to diazinon on hippocampus-dependent novel objectrecognition test performance and the expression of the N-methyl-D-aspartate (NMDA) receptor and its signal transduction pathway-related genes in the hippocampi of young adult and adult mice. Male offspring of C3H/HeN mice were subcutaneously treated with 0, 0.5, or 5 mg/kg of diazinon for 4 consecutive days beginning on postnatal day (PND) 8. Beginning on PND 46 or PND 81, a novel objectrecognition test was performed on 4 consecutive days. The hippocampi were collected on PND 50 or PND 85 after the completion of the novel objectrecognition test, and the expression levels of neurotrophins and the NMDA receptor and its signal transduction pathway-related genes were examined using real-time RT-PCR. Diazinon-injected mice exhibited a poor ability to discriminate between novel and familiar objects during both the PND 49 and the PND 84 tests. The NMDA receptor subunits NR1 and NR2B and the related protein kinase calcium/calmodulin-dependent protein kinase (CaMK)-IV and the transcription factor cyclic AMP responsive element binding protein (CREB)-1 mRNA levels were reduced in the PND 50 mice. However, no significant changes in the expressions of the NMDA subunits and their signal transduction molecules were observed in the hippocampi of the PND 85 mice. The expression level of nerve growth factor mRNA was significantly reduced in the PND 50 or 85 mice. These results indicate that neonatal diazinon exposure impaired the hippocampus-dependent novel objectrecognition ability, accompanied by a modulation in the expressions of the NMDA receptor and neurotrophin in young adult and adult mice. PMID:23212306

A character recognitionsystem is disclosed in which each character in a retina, defining a scanning raster, is scanned with random lines uniformly distributed over the retina. For each type of character to be recognized the system stores a probability density function (PDF) of the random line intersection lengths and/or a PDF of the random line number of intersections. As an unknown character is scanned, the random line intersection lengths and/or the random line number of intersections are accumulated and based on a comparison with the prestored PDFs a classification of the unknown character is performed.

Recognizing a natural object requires one to pool information from various sensory modalities, and to ignore information from competing objects. That the same semantic knowledge can be accessed through different modalities makes it possible to explore the retrieval of supramodal object concepts. Here, object-recognition processes were investigated by manipulating the relationships between sensory modalities, specifically, semantic content, and spatial alignment between auditory and visual information. Experiments were run under realistic virtual environment. Participants were asked to react as fast as possible to a target object presented in the visual and/or the auditory modality and to inhibit a distractor object (go/no-go task). Spatial alignment had no effect on object-recognition time. The only spatial effect observed was a stimulus-response compatibility between the auditory stimulus and the hand position. Reaction times were significantly shorter for semantically congruent bimodal stimuli than would be predicted by independent processing of information about the auditory and visual targets. Interestingly, this bimodal facilitation effect was twice as large as found in previous studies that also used information-rich stimuli. An interference effect was observed (i.e. longer reaction times to semantically incongruent stimuli than to the corresponding unimodal stimulus) only when the distractor was auditory. When the distractor was visual, the semantic incongruence did not interfere with objectrecognition. Our results show that immersive displays with large visual stimuli may provide large multimodal integration effects, and reveal a possible asymmetry in the attentional filtering of irrelevant auditory and visual information. PMID:19093105

The Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC) is part of the NASA Earth Science Data and Information System (ESDIS) project, responsible for archiving and distributing a wide range of terrestrial ecology data sets. Partly to enhance the recognition for scientists sharing their data, the ORNL DAAC has had a data citation policy for many years, with the citation in the name of the scientists who collected and providing an Internet URL pointing to the data set. Some journal editors, however, objected to a URL in a scientific citation, arguing that URL’s are transient and problematic for the anticipated lifetime of a scientific journal article. In response to this concern, the ORNL DAAC started assigning Digital Object Identifiers (DOIs) to published data sets in 2007 and incorporating the DOI in the requested citation for each data set. DOIs have now been assigned to all ORNL DAAC published data sets. Our experience is that the DOI is a very useful tool for finalized data sets, which is most of what the ORNL DAAC deals with and works well for managing data set citations, as well as to data sets that are updated infrequently. We have not assigned DOIs to dynamically generated data sets, such as those generated by our data subsetting tools (such as the MODIS subsetting tool and the dynamic subsets generated by OGC web services). Dynamic data sets may be a case where separating data set identification (for scientific reproducibility) from data set citation (for attribution and impact analysis) may be appropriate. DOIs have also improved our ability to track citations of data sets, both in the formal scientific literature and in documents published to the general Web. We are now seeing examples where researchers are listing published data sets on a curriculum vita, as one indication of improved recognition of the value for sharing and archiving data sets. DOIs are not yet useful for tracking and assessing

Objectrecognition is considered a necessary part in many computer vision applications. Recently, sparse coding methods, based on representing a sparse feature from an image, show remarkable results on several objectrecognition benchmarks, but the precision obtained by these methods is not yet sufficient. Such a problem arises where there are few training images available. As such, using multiple features and multitask dictionaries appears to be crucial to achieving better results. We use multitask joint sparse representation, using dynamic coefficients to connect these sparse features. In other words, we calculate the importance of each feature for each class separately. This causes the features to be used efficiently and appropriately for each class. Thus, we use variance of features and particle swarm optimization algorithms to obtain these dynamic coefficients. Experimental results of our work on Caltech-101 and Caltech-256 databases show more accuracy compared with state-of-the art ones on the same databases.

This document describes ROCIT, a neural-inspired objectrecognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to objectrecognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

Children with Autism Spectrum Disorders (ASDs) have reported to have impairments in face, recognition and face memory, but intact objectrecognition and object memory. Potential abnormalities, in these fields at the family level of high-functioning children with ASD remains understudied despite, the ever-mounting evidence that ASDs are genetic and…

The involvement of hippocampal N-methyl-d-aspartate (NMDA) receptors in the retrieval process of spontaneous objectrecognition memory was investigated. The spontaneous objectrecognition test consisted of three phases. In the sample phase, rats were exposed to two identical objects several (2-5) times in the arena. After the sample phase, various lengths of delay intervals (24h-6 weeks) were inserted (delay phase). In the test phase in which both the familiar and the novel objects were placed in the arena, rats' novel object exploration behavior under the hippocampal treatment of NMDA receptor antagonist, AP5, or vehicle was observed. With 5 exposure sessions in the sample phase (experiment 1), AP5 treatment in the test phase significantly decreased discrimination ratio when the delay was 3 weeks but not when it was one week. On the other hand, with 2 exposure sessions in the sample phase (experiment 2) in which even vehicle-injected control animals could not discriminate the novel object from the familiar one with a 3 week delay, AP5 treatment significantly decreased discrimination ratio when the delay was one week, but not when it was 24h. Additional experiment (experiment 3) showed that the hippocampal treatment of an α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist, NBQX, decreased discrimination ratio with all delay intervals tested (24h-3 weeks). Results suggest that hippocampal NMDA receptors play an important role in the retrieval of spontaneous objectrecognition memory especially when the memory trace weakens. PMID:27036649

The peptide neurotransmitter N-acetylaspartylglutamate (NAAG) is inactivated by the extracellular enzyme glutamate carboxypeptidase II. Inhibitors of this enzyme reverse dizocilpine (MK-801)-induced impairment of short-term memory in the novel objectrecognition test. The objective of this study was to test the hypothesis that NAAG peptidase inhibition enhances long-term (24h delay) memory of C57BL mice. These mice and mice in which glutamate carboxypeptidase II had been knocked out were presented with two identical objects to explore for 10min on day 1 and tested with one of these familiar objects and one novel object on day 2. Memory was assessed as the degree to which the mice recalled the familiar object and explored the novel object to a greater extent on day 2. Uninjected mice or mice injected with saline prior to the acquisition session on day 1 demonstrated a lack of memory of the acquisition experience by exploring the familiar and novel objects to the same extent on day 2. Mice treated with glutamate carboxypeptidase II inhibitors ZJ43 or 2-PMPA prior to the acquisition trial explored the novel object significantly more time than the familiar object on day 2. Consistent with these results, mice in which glutamate carboxypeptidase II had been knocked out distinguished the novel from the familiar object on day 2 while their heterozygous colony mates did not. Inhibition of glutamate carboxypeptidase II enhances recognition memory, a therapeutic action that might be useful in treatment of memory deficits related to age and neurological disorders. PMID:23200894

Pipelines to recognize 3D objects despite clutter and occlusions usually end up with a final verification stage whereby recognition hypotheses are validated or dismissed based on how well they explain sensor measurements. Unlike previous work, we propose a Global Hypothesis Verification (GHV) approach which regards all hypotheses jointly so as to account for mutual interactions. GHV provides a principled framework to tackle the complexity of our visual world by leveraging on a plurality of recognition paradigms and cues. Accordingly, we present a 3D objectrecognition pipeline deploying both global and local 3D features as well as shape and color. Thereby, and facilitated by the robustness of the verification process, diverse object hypotheses can be gathered and weak hypotheses need not be suppressed too early to trade sensitivity for specificity. Experiments demonstrate the effectiveness of our proposal, which significantly improves over the state-of-art and attains ideal performance (no false negatives, no false positives) on three out of the six most relevant and challenging benchmark datasets. PMID:26485476

Sidescan sonar is increasingly accepted as the sensor of choice for sea minehunting over large areas in shallow water. Automatic Target Recognition (ATR) algorithms are therefore being developed to assist and, in the case of autonomous vehicles, even replace the human operator as the primary recognition agent deciding whether an object in the sonar imagery is a mine or simply benign seafloor clutter. Whether ATR aids or replaces a human operator, a natural benchmark for judging the quality of ATR is the unaided human performance when ATR is not used. The benchmark can help when estimating the performance benefit (or cost) of switching from human to automatic recognition for instance, or when planning how human and machine should best interact in cooperative search operations. This paper reports a human performance study using a large library of real sonar images collected for the development and testing of ATR algorithms. The library features 234 mine-like man-made objects deployed for the purpose, as well as 105 instances of naturally occurring clutter. The human benchmark in this case is the average of ten human subjects expressed in terms of a receiver operating characteristic (ROC) curve. An ATR algorithm for man-made/natural object discrimination is also tested and compared with the human benchmark . The implications of its relative performance for the integration of ATR are considered.

Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based objectrecognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate "early" classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches. PMID:25532151

Human eyes cannot notice low contrast objects in the image. Image contrast enhancement methods can make the unnoticed objects noticed, and human can detect and recognize the objects. In order to guide the design of enhancement methods, performance of enhancement methods for objects detection and recognition(ODR) should be valued. The existing performance evaluation methods evaluate image enhancement methods by calculating the increment of contrast or image information entropy. However, it is essentially an image information transmission process that human detect and recognize objects in the image, and image contrast enhancement can be viewed as a form of image coding. According to human visual properties, the transmission process of ODR information are modeled in this paper, and a performance evaluation method was proposed from the information theory of Shannon.

Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognitionsystem. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate.

This report describes an automatic target recognitionsystem for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system uses image data fusion and gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. This volume gives a general description of the ATR system.

Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented. PMID:17170477

Most common approaches to interactive objectrecognition in multisensor/multispectral imagery are sensor data driven. They address the problem of displaying images of multiple sensor sources in a manner adequate to the characteristics of the sensors. Fusion of sensed data is the topic of those concepts. This paper discusses a supplementing approach from the opposite end: the domain of target objects. Knowledge about the appearance of objects under various spectral conditions guides the image analyst through the interpretation process. Therefore, the basic concept of an >>interactive recognition assistant<< will be proposed. Starting from a set of candidate objects the image analyst is guided through a step-by-step interpretation process by getting indicated the respectively most significant features for efficient reduction of the candidate set. In the context of this approach we discuss the question of modeling and storing the multisensorial appearances of target objects as well as the problem of an adequate dynamic human-machine-interface that takes into account the mental model of human image interpretation.

A methodology for automatically detecting symptoms of frequently occurring errors in large computer systems is developed. The proposed symptom recognition methodology and its validation are based on probabilistic techniques. The technique is shown to work on real failure data from two CYBER systems at the University of Illinois. The methodology allows for the resolution between independent and dependent causes and, also quantifies a measure of the strength of relationship among errors. Comparison made with failure/repair information obtained from field maintenance engineers shows that in 85% of the cases, the error symptoms recognized by our approach correspond to real system problems. Further, the remaining 15% although not directly supported by field data, were confirmed as valid problems. Some of these were shown to be persistent problems which otherwise would have been considered as minor transients and hence ignored.

This report describes the software of an automatic target recognitionsystem (version 14), from a programmer`s point of view. The intent is to provide information that will help people who wish to modify the software. In separate volumes are a general description of the ATR system, Automatic TLI RecognitionSystem, General Description, and a user`s manual, Automatic TLI RecognitionSystem, User`s Guide. 2 refs.

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, formore » a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.« less

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

Perception emerges from a dynamic interplay between feed-forward sensory input and feedback modulation along the cascade of neural processing. Prior knowledge, a major form of top-down modulatory signal, benefits perception by enabling efficacious inference and resolving ambiguity, particularly under circumstances of degraded visual input. Despite semantic information being a potentially critical source of this top-down influence, to date, the core neural substrate of semantic knowledge (the anterolateral temporal lobe - ATL) has not been considered as a key component of the feedback system. Here we provide direct evidence of its significance for visual cognition - the ATL underpins the semantic aspect of objectrecognition, amalgamating sensory-based (amount of accumulated sensory input) and semantic-based (representational proximity between exemplars and typicality of appearance) influences. Using transcranial theta-burst stimulation combined with a novel visual identification paradigm, we demonstrate that the left ATL contributes to discrimination between visual objects. Crucially, its contribution is especially vital under situations where semantic knowledge is most needed for supplementing deficiency of input (brief visual exposure), discerning analogously-coded exemplars (close representational distance), and resolving discordance (target appearance violating the statistical typicality of its category). Our findings characterise functional properties of the ATL in objectrecognition: this neural structure is summoned to augment the visual system when the latter is overtaxed by challenging conditions (insufficient input, overlapped neural coding, and conflict between incoming signal and expected configuration). This suggests a need to revisit current theories of objectrecognition, incorporating the ATL that interfaces high-level vision with semantic knowledge. PMID:27088615

We measured infants’ recognition of familiar and unfamiliar 3-D objects and their 2-D representations using event-related potentials (ERPs). Infants differentiated familiar from unfamiliar objects when viewing them in both two and three dimensions. However, differentiation between the familiar and novel objects occurred more quickly when infants viewed the object in 3-D than when they viewed 2-D representations. The results are discussed with respect to infants’ recognition abilities and their understanding of real objects and representations. This is the first study using 3-D objects in conjunction with ERPs in infants, and it introduces an interesting new methodology for assessing infants’ electrophysiological responses to real objects. PMID:16445396

Background: Cognitive decline or dementia is a debilitating problem of neurological disorders such as Alzheimer's and Parkinson's disease, including special conditions like chemobrain. Dietary flavonoids proved to be efficacious in delaying the incidence of neurodegenerative diseases. Two such flavonoids, naringin (NAR) and rutin (RUT) were reported to have neuroprotective potential with beneficial effects on spatial and emotional memories in particular. However, the efficacy of these flavonoids is poorly understood on episodic memory, which comprises an important form of autobiographical memory. Objective: This study objective is to evaluate NAR and RUT to reverse time-delay-induced long-term and scopolamine-induced short-term episodic memory deficits in Wistar rats. Materials and Methods: We have evaluated both short-term and long-term episodic memory forms using novel objectrecognition task. Open field paradigm was used to assess locomotor activity for any confounding influence on memory assessment. Donepezil was used as positive control and was effective in both models at 1 mg/kg, i.p. Results: Animals treated with NAR and RUT at 50 and 100 mg/kg, p.o. spent significantly more time exploring novel object compared to familiar one, whereas control animals spent almost equal time with both objects in choice trial. NAR and RUT dose-dependently increased recognition and discriminative indices in time-induced long-term as well as scopolamine-induced short-term episodic memory deficit models without interfering with the locomotor activity. Conclusion: We conclude that, NAR and RUT averted both short- and long-term episodic memory deficits in Wistar rats, which may be potential interventions for neurodegenerative diseases as well as chemobrain condition. SUMMARY Incidence of Alzheimer's disease is increasing globally and the current therapy is only symptomatic. Curative treatment is a major lacuna. NAR and RUT are natural flavonoids proven for their pleiotropic

How do we attend to objects at a variety of sizes as we view our visual world? Because of an advantage in identification of lowpass over highpass filtered patterns, as well as large over small images, a number of theorists have assumed that size-independent recognition is achieved by spatial frequency (SF) based coarse-to-fine tuning. We found that the advantage of large sizes or low SFs was lost when participants attempted to identify a target object (specified verbally) somewhere in the middle of a sequence of 40 images of objects, each shown for only 72 ms, as long as the target and distractors were the same size or spatial frequency (unfiltered or low or high bandpassed). When targets were of a different size or scale than the distractors, a marked advantage (pop out) was observed for large (unfiltered) and low SF targets against small (unfiltered) and high SF distractors, respectively, and a marked decrement for the complementary conditions. Importantly, this pattern of results for large and small images was unaffected by holding absolute or relative SF content constant over the different sizes and it could not be explained by simple luminance- or contrast-based pattern masking. These results suggest that size/scale tuning in objectrecognition was accomplished over the first several images (<576 ms) in the sequence and that the size tuning was implemented by a mechanism sensitive to spatial extent rather than to variations in spatial frequency. PMID:11412885

Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater objectrecognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater objectrecognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.

Ampakines are a class of putative nootropic drug designed to positively modulate the AMPA receptor and have been investigated as a potential treatment for cognitive disorders such as Alzheimer's Disease. Nonetheless, some ampakines such as CX717 have been incompletely characterized in behavioural pharmacological studies. Therefore, in this study, we attempted to further characterize the effects of the ampakine, CX717 (20 mg/kg s.c), on the performance of rats in a 5 choice serial reaction time (5CSRTT) and objectrecognition memory task, using rats with cognitive deficits caused by bilateral vestibular deafferentation (BVD) as a model. In the 5CSRTT, when the stimulus duration was varied from 5 to 2 sec, the number of incorrect responses was significantly greater for the BVD group compared to sham controls, but significantly less for the CX717 groups, with no significant interaction. With changes in inter-trial interval (ITI), there was a significant effect of surgery/drug and a significant effect of ITI on premature responses, and the BVD group treated with CX717 showed significantly fewer premature responses than the other groups. In the objectrecognition memory task, CX717 significantly reduced total exploration time and the exploration towards the novel object in both sham and BVD animals. These results suggest that CX717 can reduce the number of incorrect responses in both sham and BVD rats and enhance inhibitory control specifically in BVD rats, in the 5CSRTT. On the other hand, CX717 produced a detrimental effect in the objectrecognition memory task. PMID:22171951

Individual differences in face recognition are often contrasted with differences in objectrecognition using a single object category. Likewise, individual differences in perceptual expertise for a given object domain have typically been measured relative to only a single category baseline. In Experiment 1, we present a new test of objectrecognition, the Vanderbilt Expertise Test (VET), which is comparable in methods to the Cambridge Face Memory Task (CFMT) but uses eight different object categories. Principal component analysis reveals that the underlying structure of the VET can be largely explained by two independent factors, which demonstrate good reliability and capture interesting sex differences inherent in the VET structure. In Experiment 2, we show how the VET can be used to separate domain-specific from domain-general contributions to a standard measure of perceptual expertise. While domain-specific contributions are found for car matching for both men and women and for plane matching in men, women in this sample appear to use more domain-general strategies to match planes. In Experiment 3, we use the VET to demonstrate that holistic processing of faces predicts face recognition independently of general objectrecognition ability, which has a sex-specific contribution to face recognition. Overall, the results suggest that the VET is a reliable and valid measure of objectrecognition abilities and can measure both domain-general skills and domain-specific expertise, which were both found to depend on the sex of observers. PMID:22877929

This paper describes an optical music recognitionsystem composed of a database and three interdependent processes: a recognizer, an editor, and a learner. Given a scanned image of a musical score, the recognizer locates, separates, and classifies symbols into musically meaningful categories. This classification is based on the k-nearest neighbor method using a subset of the database that contains features of symbols classified in previous recognition sessions. Output of the recognizer is corrected by a musically trained human operator using a music notation editor. The editor provides both visual and high-quality audio feedback of the output. Editorial corrections made by the operator are passed to the learner which then adds the newly acquired data to the database. The learner's main task, however, involves selecting a subset of the database and reweighing the importance of the features to improve accuracy and speed for subsequent sessions. Good preliminary results have been obtained with everything from professionally engraved scores to hand-written manuscripts.

The novel objectrecognition (NOR) test is a widely-used paradigm to study learning and memory in rodents. NOR performance is typically measured as the preference to interact with a novel object over a familiar object based on spontaneous exploratory behaviour. In rats and mice, females usually have greater NOR ability than males. The NOR test is now available for a large number of species, including fish, but sex differences have not been properly tested outside of rodents. We compared male and female guppies (Poecilia reticulata) in a NOR test to study whether sex differences exist also for fish. We focused on sex differences in both performance and behaviour of guppies during the test. In our experiment, adult guppies expressed a preference for the novel object as most rodents and other species do. When we looked at sex differences, we found the two sexes showed a similar preference for the novel object over the familiar object, suggesting that male and female guppies have similar NOR performances. Analysis of behaviour revealed that males were more inclined to swim in the proximity of the two objects than females. Further, males explored the novel object at the beginning of the experiment while females did so afterwards. These two behavioural differences are possibly due to sex differences in exploration. Even though NOR performance is not different between male and female guppies, the behavioural sex differences we found could affect the results of the experiments and should be carefully considered when assessing fish memory with the NOR test. PMID:27305102

The novel objectrecognition (NOR) test is a widely-used paradigm to study learning and memory in rodents. NOR performance is typically measured as the preference to interact with a novel object over a familiar object based on spontaneous exploratory behaviour. In rats and mice, females usually have greater NOR ability than males. The NOR test is now available for a large number of species, including fish, but sex differences have not been properly tested outside of rodents. We compared male and female guppies (Poecilia reticulata) in a NOR test to study whether sex differences exist also for fish. We focused on sex differences in both performance and behaviour of guppies during the test. In our experiment, adult guppies expressed a preference for the novel object as most rodents and other species do. When we looked at sex differences, we found the two sexes showed a similar preference for the novel object over the familiar object, suggesting that male and female guppies have similar NOR performances. Analysis of behaviour revealed that males were more inclined to swim in the proximity of the two objects than females. Further, males explored the novel object at the beginning of the experiment while females did so afterwards. These two behavioural differences are possibly due to sex differences in exploration. Even though NOR performance is not different between male and female guppies, the behavioural sex differences we found could affect the results of the experiments and should be carefully considered when assessing fish memory with the NOR test. PMID:27305102

Perinatal asphyxia (PA) is associated with long-term neuronal damage and cognitive deficits in adulthood, such as learning and memory disabilities. After PA, specific brain regions are compromised, including neocortex, hippocampus, basal ganglia, and ascending neuromodulatory pathways, such as dopamine system, explaining some of the cognitive disabilities. We hypothesize that other neuromodulatory systems, such as histamine system from the tuberomammillary nucleus (TMN), which widely project to telencephalon, shown to be relevant for learning and memory, may be compromised by PA. We investigated here the effect of PA on (i) Density and neuronal activity of TMN neurons by double immunoreactivity for adenosine deaminase (ADA) and c-Fos, as marker for histaminergic neurons and neuronal activity respectively. (ii) Expression of the histamine-synthesizing enzyme, histidine decarboxylase (HDC) by western blot and (iii) thioperamide an H3 histamine receptor antagonist, on an objectrecognition memory task. Asphyxia-exposed rats showed a decrease of ADA density and c-Fos activity in TMN, and decrease of HDC expression in hypothalamus. Asphyxia-exposed rats also showed a low performance in objectrecognition memory compared to caesarean-delivered controls, which was reverted in a dose-dependent manner by the H3 antagonist thioperamide (5-10mg/kg, i.p.). The present results show that the histaminergic neuronal system of the TMN is involved in the long-term effects induced by PA, affecting learning and memory. PMID:27444242

Objective. The human auditory system acquires environmental information under sound stimuli faster than visual or touch systems, which in turn, allows for faster human responses to such stimuli. It also complements senses such as sight, where direct line-of-view is necessary to identify objects, in the environment recognition process. This work focuses on implementing human reaction to sound stimuli and environment recognition on assistive robotic devices, such as robotic wheelchairs or robotized cars. These vehicles need environment information to ensure safe navigation. Approach. In the field of environment recognition, range sensors (such as LiDAR and ultrasonic systems) and artificial vision devices are widely used; however, these sensors depend on environment constraints (such as lighting variability or color of objects), and sound can provide important information for the characterization of an environment. In this work, we propose a sound-based approach to enhance the environment recognition process, mainly for cases that compromise human integrity, according to the International Classification of Functioning (ICF). Our proposal is based on a neural network implementation that is able to classify up to 15 different environments, each selected according to the ICF considerations on environment factors in the community-based physical activities of people with disabilities. Main results. The accuracy rates in environment classification ranges from 84% to 93%. This classification is later used to constrain assistive vehicle navigation in order to protect the user during daily activities. This work also includes real-time outdoor experimentation (performed on an assistive vehicle) by seven volunteers with different disabilities (but without cognitive impairment and experienced in the use of wheelchairs), statistical validation, comparison with previously published work, and a discussion section where the pros and cons of our system are evaluated. Significance

In this paper we present an object instance retrieval approach. The baseline approach consists of a pool of image features which are computed on the bounding boxes of a query object track and compared to a database of tracks in order to find additional appearances of the same object instance. We improve over this simple baseline approach in multiple ways: 1) we include motion cues to achieve improved robustness to viewpoint and rotation changes, 2) we include operator feedback to iteratively re-rank the resulting retrieval lists and 3) we use operator feedback and location constraints to train classifiers and learn an instance specific appearance model. We use these classifiers to further improve the retrieval results. The approach is evaluated on two popular public datasets for two different applications. We evaluate person re-identification on the CAVIAR shopping mall surveillance dataset and vehicle instance recognition on the VIVID aerial dataset and achieve significant improvements over our baseline results.

Previous studies have shown that dopamine D1 receptor antagonists impair novel objectrecognition memory but the effects of dopamine D1 receptor stimulation remain to be determined. This study investigated the effects of the selective dopamine D1 receptor agonist SKF81297 on acquisition and retrieval in the novel objectrecognition task in male Wistar rats. SKF81297 (0.4 and 0.8 mg/kg s.c.) given 15 min before the sampling phase impaired novel objectrecognition evaluated 10 min or 24 h later. The same treatments also reduced novel objectrecognition memory tested 24 h after the sampling phase and when given 15 min before the choice session. These data indicate that D1 receptor stimulation modulates both the encoding and retrieval of objectrecognition memory. Microinfusion of SKF81297 (0.025 or 0.05 μg/side) into the prelimbic sub-region of the medial prefrontal cortex (mPFC) in this case 10 min before the sampling phase also impaired novel objectrecognition memory, suggesting that the mPFC is one important site mediating the effects of D1 receptor stimulation on visual recognition memory. PMID:26277743

Little is known on cross-modal interaction in complex objectrecognition. The factors influencing this interaction were investigated using simultaneous presentation of pictures and vocalizations of animals. In separate blocks, the task was to identify either the visual or the auditory stimulus, ignoring the other modality. The pictures and the sounds were congruent (same animal), incongruent (different animals) or neutral (animal with meaningless stimulus). Performance in congruent trials was better than in incongruent trials, regardless of whether subjects attended the visual or the auditory stimuli, but the effect was larger in the latter case. This asymmetry persisted with addition of a long delay after the stimulus and before the response. Thus, the asymmetry cannot be explained by a lack of processing time for the auditory stimulus. However, the asymmetry was eliminated when low-contrast visual stimuli were used. These findings suggest that when visual stimulation is highly informative, it affects auditory recognition more than auditory stimulation affects visual recognition. Nevertheless, this modality dominance is not rigid; it is highly influenced by the quality of the presented information. PMID:19066869

The role of sensory-motor representations in objectrecognition was investigated in experiments involving AD, a patient with mild visual agnosia who was impaired in the recognition of visually presented living as compared to non-living entities. AD named visually presented items for which sensory-motor information was available significantly more…

Clinical trials with blind patients implanted with a visual neuroprosthesis showed that even the simplest tasks were difficult to perform with the limited vision restored with current implants. Simulated prosthetic vision (SPV) is a powerful tool to investigate the putative functions of the upcoming generations of visual neuroprostheses. Recent studies based on SPV showed that several generations of implants will be required before usable vision is restored. However, none of these studies relied on advanced image processing. High-level image processing could significantly reduce the amount of information required to perform visual tasks and help restore visuomotor behaviors, even with current low-resolution implants. In this study, we simulated a prosthetic vision device based on object localization in the scene. We evaluated the usability of this device for objectrecognition, localization, and reaching. We showed that a very low number of electrodes (e.g., nine) are sufficient to restore visually guided reaching movements with fair timing (10 s) and high accuracy. In addition, performance, both in terms of accuracy and speed, was comparable with 9 and 100 electrodes. Extraction of high level information (objectrecognition and localization) from video images could drastically enhance the usability of current visual neuroprosthesis. We suggest that this method-that is, localization of targets of interest in the scene-may restore various visuomotor behaviors. This method could prove functional on current low-resolution implants. The main limitation resides in the reliability of the vision algorithms, which are improving rapidly. PMID:25900238

The novel objectrecognition test (NOR) test is a two trial cognitive paradigm that assesses recognition memory. Recognition memory is disturbed in a range of human disorders and NOR is widely used in rodents for investigating deficits in a variety of animal models of human conditions where cognition is impaired. It possesses several advantages over more complex tasks that involve lengthy training procedures and/or food or water deprivation. It is quick to administer, non-rewarded, provides data quickly, cost effective and most importantly, ethologically relevant as it relies on the animal's natural preference for novelty. A PubMed search revealed over 900 publications in rats and mice using this task over the past 3 years with 34 reviews in the past 10 years, demonstrating its increasing popularity with neuroscientists. Although it is widely used in many disparate areas of research, no articles have systematically examined this to date, which is the subject of our review. We reveal that NOR may be used to study recognition memory deficits that occur in Alzheimer's disease and schizophrenia, where research is extensive, in Parkinson's disease and Autism Spectrum Disorders (ASD) where we observed markedly reduced numbers of publications. In addition, we review the use of NOR to study cognitive deficits induced by traumatic brain injury and cancer chemotherapy, not disorders per se, but situations in which cognitive deficits dramatically reduce the quality of life for those affected, see Fig. 1 for a summary. Our review reveals that, in all these animal models, the NOR test is extremely useful for identification of the cognitive deficits observed, their neural basis, and for testing the efficacy of novel therapeutic agents. Our conclusion is that NOR is of considerable value for cognitive researchers of all disciplines and we anticipate that its use will continue to increase due to its versatility and several other advantages, as detailed in this review. PMID

Recognition of metal cations by biological systems can be compared with the geochemical criteria for isomorphous replacement. Biological systems are more highly selective and much more rapid. Methods of maintaining an optimum concentration, including storage and transfer for the essential trace elements, copper and iron, used in some organisms are in part reproducible by coordination chemists while other features have not been reporduced in models. Poisoning can result from a foreign metal taking part in a reaction irreversibly so that the recognition site or molecule is not released. For major nutrients, sodium, potassium, magnesium and calcium, there are similarities to the trace metals in selective uptake but differences qualitatively and quantitatively in biological activity. Compounds selective for potassium replace all the solvation sphere with a symmetrical arrangement of oxygen atoms; those selective for sodium give an asymmetrical environment with retention of a solvent molecule. Experiments with naturally occurring antibiotics and synthetic model compounds have shown that flexibility is an important feature of selectivity and that for transfer or carrier properties there is an optimum (as opposed to a maximum) metal-ligand stability constant. Thallium is taken up instead of potassium and will activate some enzymes; it is suggested that the poisonous characteristics arise because the thallium ion may bind more strongly than potassium to part of a site and then fail to bind additional atoms as required for the biological activity. Criteria for the design of selective complexing agents are given with indications of those which might transfer more than one metal at once. PMID:1815

In recent years, a credit recognitionsystem has been developing in Chinese higher education institutions. Much research has been done on this development, but it has been concentrated on system building, barriers/issues and international practices. The relationship between credit recognitionsystem reforms and democratisation of higher education…

Current research on computer vision often needs specific techniques for particular problems. Little use has been made of high-level aspects of computer vision, such as three-dimensional (3D) objectrecognition, that are appropriate for large classes of problems and situations. In particular, high-level vision often focuses mainly on the extraction of symbolic descriptions, and pays little attention to the speed of processing. In order to extract and recognize target intelligently and rapidly, in this paper we developed a new 3D target recognition method based on inherent feature of objects in which cuboid was taken as model. On the basis of analysis cuboid nature contour and greyhound distributing characteristics, overall fuzzy evaluating technique was utilized to recognize and segment the target. Then Hough transform was used to extract and match model's main edges, we reconstruct aim edges by stereo technology in the end. There are three major contributions in this paper. Firstly, the corresponding relations between the parameters of cuboid model's straight edges lines in an image field and in the transform field were summed up. By those, the aimless computations and searches in Hough transform processing can be reduced greatly and the efficiency is improved. Secondly, as the priori knowledge about cuboids contour's geometry character known already, the intersections of the component extracted edges are taken, and assess the geometry of candidate edges matches based on the intersections, rather than the extracted edges. Therefore the outlines are enhanced and the noise is depressed. Finally, a 3-D target recognition method is proposed. Compared with other recognition methods, this new method has a quick response time and can be achieved with high-level computer vision. The method present here can be used widely in vision-guide techniques to strengthen its intelligence and generalization, which can also play an important role in object tracking, port AGV, robots

Standard objectrecognition procedures assess animals' memory through their spontaneous exploration of novel objects or novel configurations of objects with other aspects of their environment. Such tasks are widely used in memory research, but also in pharmaceutical companies screening new drug treatments. However, behaviour in these tasks may be driven by influences other than novelty such as stress from handling which can subsequently influence performance. This extra-experimental variance means that large numbers of animals are required to maintain power. In addition, accumulation of data is time consuming as animals typically perform only one trial per day. The present study aimed to explore how effectively recognition memory could be tested with a new continual trials apparatus which allows for multiple trials within a session and reduced handling stress through combining features of delayed nonmatching-to-sample and spontaneous objectrecognition tasks. In this apparatus Lister hooded rats displayed performance significantly above chance levels in objectrecognition tasks (Experiments 1 and 2) and in tasks of object-location (Experiment 3) and object-in-context memory (Experiment 4) with data from only five animals or fewer per experimental group. The findings indicated that the results were comparable to those of previous reports in the literature and maintained statistical power whilst using less than a third of the number of animals typically used in spontaneous recognition paradigms. Overall, the results highlight the potential benefit of the continual trials apparatus to reduce the number of animals used in recognition memory tasks. PMID:22917958

We present an automatic road sign detection and recognition service system for mobile devices. The system is based on a client-server architecture which allows mobile users to take pictures of road signs and request detection and recognition service from a centralized server for processing. The preprocessing, detection and recognition take place at the server end and consequently, the result is sent back to the mobile device. For road sign detection, we use particular color features calculated from the input image. Recognition is implemented using a neural network based on normalized color histogram features. We report on the effects of various parameters on recognition accuracy. Our results demonstrate that the system can provide an efficient framework for locale-dependent road sign recognition with multilingual support.

In the present study we investigated long-term memory for unpleasant, neutral and spider pictures in 15 spider-fearful and 15 non-fearful control individuals using behavioral and electrophysiological measures. During the initial (incidental) encoding, pictures were passively viewed in three separate blocks and were subsequently rated for valence and arousal. A recognition memory task was performed one week later in which old and new unpleasant, neutral and spider pictures were presented. Replicating previous results, we found enhanced memory performance and higher confidence ratings for unpleasant when compared to neutral materials in both animal fearful individuals and controls. When compared to controls high animal fearful individuals also showed a tendency towards better memory accuracy and significantly higher confidence during recognition of spider pictures, suggesting that memory of objects prompting specific fear is also facilitated in fearful individuals. In line, spider-fearful but not control participants responded with larger ERP positivity for correctly recognized old when compared to correctly rejected new spider pictures, thus showing the same effects in the neural signature of emotional memory for feared objects that were already discovered for other emotional materials. The increased fear memory for phobic materials observed in the present study in spider-fearful individuals might result in an enhanced fear response and reinforce negative beliefs aggravating anxiety symptomatology and hindering recovery. PMID:25296032

In the present study we investigated long-term memory for unpleasant, neutral and spider pictures in 15 spider-fearful and 15 non-fearful control individuals using behavioral and electrophysiological measures. During the initial (incidental) encoding, pictures were passively viewed in three separate blocks and were subsequently rated for valence and arousal. A recognition memory task was performed one week later in which old and new unpleasant, neutral and spider pictures were presented. Replicating previous results, we found enhanced memory performance and higher confidence ratings for unpleasant when compared to neutral materials in both animal fearful individuals and controls. When compared to controls high animal fearful individuals also showed a tendency towards better memory accuracy and significantly higher confidence during recognition of spider pictures, suggesting that memory of objects prompting specific fear is also facilitated in fearful individuals. In line, spider-fearful but not control participants responded with larger ERP positivity for correctly recognized old when compared to correctly rejected new spider pictures, thus showing the same effects in the neural signature of emotional memory for feared objects that were already discovered for other emotional materials. The increased fear memory for phobic materials observed in the present study in spider-fearful individuals might result in an enhanced fear response and reinforce negative beliefs aggravating anxiety symptomatology and hindering recovery. PMID:25296032

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain. PMID:12141512

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.

In this paper, a noble nonintrusive three-dimensional (3D) face modeling system for random-profile-based 3D face recognition is presented. Although recent two-dimensional (2D) face recognitionsystems can achieve a reliable recognition rate under certain conditions, their performance is limited by internal and external changes, such as illumination and pose variation. To address these issues, 3D face recognition, which uses 3D face data, has recently received much attention. However, the performance of 3D face recognition highly depends on the precision of acquired 3D face data, while also requiring more computational power and storage capacity than 2D face recognitionsystems. In this paper, we present a developed nonintrusive 3D face modeling system composed of a stereo vision system and an invisible near-infrared line laser, which can be directly applied to profile-based 3D face recognition. We further propose a novel random-profile-based 3D face recognition method that is memory-efficient and pose-invariant. The experimental results demonstrate that the reconstructed 3D face data consists of more than 50 k 3D point clouds and a reliable recognition rate against pose variation. PMID:24691101

A new technique is presented for the three dimensional recognition of symmetric objects from range images. Beginning from the implicit representation of quadrics, a set of ten coefficients is determined for symmetric objects like spheres, cones, cylinders, ellipsoids, and parallelepipeds. Instead of using these ten coefficients trying to fit them to smooth surfaces (patches) based on the traditional way of determining curvatures, a new approach based on two dimensional geometry is used. For each symmetric object, a unique set of two dimensional curves is obtained from the various angles at which the object is intersected with a plane. Using the same ten coefficients obtained earlier and based on the discriminant method, each of these curves is classified as a parabola, circle, ellipse, or hyperbola. Each symmetric object is found to possess a unique set of these two dimensional curves whereby it can be differentiated from the others. It is shown that instead of using the three dimensional discriminant which involves evaluation of the rank of its matrix, it is sufficient to use the two dimensional discriminant which only requires three arithmetic operations.

A sequential matching task was used to compare how the difficulty of shape discrimination influences the achievement of object constancy for depth rotations across haptic and visual objectrecognition. Stimuli were nameable, 3-dimensional plastic models of familiar objects (e.g., bed, chair) and morphs midway between these endpoint shapes (e.g., a…

In recent years, various gesture recognitionsystems have been studied for use in television and video games[1]. In such systems, motion areas ranging from 1 to 3 meters deep have been evaluated[2]. However, with the burgeoning popularity of small mobile displays, gesture recognitionsystems capable of operating at much shorter ranges have become necessary. The problems related to such systems are exacerbated by the fact that the camera's field of view is unknown to the user during operation, which imposes several restrictions on his/her actions. To overcome the restrictions generated from such mobile camera devices, and to create a more flexible gesture recognition interface, we propose a hybrid hand gesture system, in which two types of gesture recognition modules are prepared and with which the most appropriate recognition module is selected by a dedicated switching module. The two recognition modules of this system are shape analysis using a boosting approach (detection-based approach)[3] and motion analysis using image frame differences (motion-based approach)(for example, see[4]). We evaluated this system using sample users and classified the resulting errors into three categories: errors that depend on the recognition module, errors caused by incorrect module identification, and errors resulting from user actions. In this paper, we show the results of our investigations and explain the problems related to short-range gesture recognitionsystems.

Recently, our laboratory reported that exposure to nanoparticle-rich diesel exhaust (NRDE) for 3 months impaired hippocampus-dependent spatial learning ability and up-regulated the expressions of memory function-related genes in the hippocampus of female mice. However, whether NRDE affects the hippocampus-dependent non-spatial learning ability and the mechanism of NRDE-induced neurotoxicity was unknown. Female BALB/c mice were exposed to clean air, middle-dose NRDE (M-NRDE, 47 μg/m{sup 3}), high-dose NRDE (H-NRDE, 129 μg/m{sup 3}), or filtered H-NRDE (F-DE) for 3 months. We then investigated the effect of NRDE exposure on non-spatial learning ability and the expression of genes related to glutamate neurotransmission using a novel objectrecognition test and a real-time RT-PCR analysis, respectively. We also examined microglia marker Iba1 immunoreactivity in the hippocampus using immunohistochemical analyses. Mice exposed to H-NRDE or F-DE could not discriminate between familiar and novel objects. The control and M-NRDE-exposed groups showed a significantly increased discrimination index, compared to the H-NRDE-exposed group. Although no significant changes in the expression levels of the NMDA receptor subunits were observed, the expression of glutamate transporter EAAT4 was decreased and that of glutamic acid decarboxylase GAD65 was increased in the hippocampus of H-NRDE-exposed mice, compared with the expression levels in control mice. We also found that microglia activation was prominent in the hippocampal area of the H-NRDE-exposed mice, compared with the other groups. These results indicated that exposure to NRDE for 3 months impaired the novel objectrecognition ability. The present study suggests that genes related to glutamate metabolism may be involved in the NRDE-induced neurotoxicity observed in the present mouse model. -- Highlights: ► The effects of nanoparticle-induced neurotoxicity remain unclear. ► We investigated the effect of exposure to

The problem was to determine the ability of a speech recognizer to extract prosodic speech features, such as pitch and stress, and to examine these features for application to future voice recognitionsystems. The Speech Systems Incorporated (SSI) speech recognizer demonstrated that it could detect prosodic features and that these features do indicate the word and/or syllable that is stressed by the speaker. The research examined the effect of prosodics, such as pitch, amplitude, and duration, on word and syllable stress by using the SSI. Subjects read phases and sentences, using a given intonation and stress. The three sections of the experiment compared questions and answers, words stressed within a sentence, and noun/verb pairs, such as object and subject. The results were analyzed both on the syllable level and the word level. In all cases, there was a significant increase in pitch, amplitude, and duration when comparing stressed words and syllables to unstressed words and syllables. When comparing unstressed words only, it was also noted that the first word in a sentence has an increase in pitch, amplitude, and duration. The threshold could be set in recognitionsystems for each of these parameters. Current speech recognizers do not use acoustic data above the word level. This research shows that we have the capability of developing better speech systems by incorporating prosodics with new linguistic software.

Recently, our laboratory reported that exposure to nanoparticle-rich diesel exhaust (NRDE) for 3 months impaired hippocampus-dependent spatial learning ability and up-regulated the expressions of memory function-related genes in the hippocampus of female mice. However, whether NRDE affects the hippocampus-dependent non-spatial learning ability and the mechanism of NRDE-induced neurotoxicity was unknown. Female BALB/c mice were exposed to clean air, middle-dose NRDE (M-NRDE, 47 μg/m(3)), high-dose NRDE (H-NRDE, 129 μg/m(3)), or filtered H-NRDE (F-DE) for 3 months. We then investigated the effect of NRDE exposure on non-spatial learning ability and the expression of genes related to glutamate neurotransmission using a novel objectrecognition test and a real-time RT-PCR analysis, respectively. We also examined microglia marker Iba1 immunoreactivity in the hippocampus using immunohistochemical analyses. Mice exposed to H-NRDE or F-DE could not discriminate between familiar and novel objects. The control and M-NRDE-exposed groups showed a significantly increased discrimination index, compared to the H-NRDE-exposed group. Although no significant changes in the expression levels of the NMDA receptor subunits were observed, the expression of glutamate transporter EAAT4 was decreased and that of glutamic acid decarboxylase GAD65 was increased in the hippocampus of H-NRDE-exposed mice, compared with the expression levels in control mice. We also found that microglia activation was prominent in the hippocampal area of the H-NRDE-exposed mice, compared with the other groups. These results indicated that exposure to NRDE for 3 months impaired the novel objectrecognition ability. The present study suggests that genes related to glutamate metabolism may be involved in the NRDE-induced neurotoxicity observed in the present mouse model. PMID:22659509

In this paper, we propose a region-based objectrecognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, the values of SPCNN parameters are automatically set by our previously proposed method in terms of each object model. In order to reduce various light intensity effects and take advantage of SPCNN high resolution on low intensities for achieving optimized color segmentation, a transformation integrating normalized Red Green Blue (RGB) with opponent color spaces is introduced. A novel image segmentation strategy is suggested to group the pixels firing synchronously throughout all the transformed channels of an image. Based on the segmentation results, a series of adaptive thresholds, which is adjustable according to the specific object model is employed to remove outlier region blobs, form potential clusters, and refine the clusters in test images. The proposed SPCNN-RBOR method overcomes the drawback of feature-based methods that inevitably includes background information into local invariant feature descriptors when keypoints locate near object boundaries. A large number of experiments have proved that the proposed SPCNN-RBOR method is robust for diverse complex variations, even under partial occlusion and highly cluttered environments. In addition, the SPCNN-RBOR method works well in not only identifying textured objects, but also in less-textured ones, which significantly outperforms the current feature-based methods. PMID:25494514

Objectrecognition can be parametrized systematically through physically robust wave objects by linking features (observables) in scattered field data with features on the object (target) giving rise to the data. The wave objects are broadly separated into global (mode) and local (wavefront) categories. Their parametrization requires different wave-oriented signal- processing algorithms which are implemented conveniently in relevant subdomains of the configuration (space-time) spectrum (wavenumber-frequency) phase space. Projection of scattering data onto the phase space is achieved via Gaussian-windowed Fourier transforms, wavelet transforms, and windowed model-based (superresolution) algorithms. Example results are presented here for time-domain modes excited by an open cavity as well as by periodic and quasi-periodic structures, with data processed in the time-frequency phase space. Additionally, we consider frequency-domain modes (leaky modes supported by a dielectric slab) which are processed in the space-wavenumber phase space. For some situations, it is more appropriate to process the entire database simultaneously (without windowing), and we have used such techniques for certain modal and wavefront parametrizations. Concerning modal 'footprinting', results are presented for superresolution processing of measured short-pulse scattering data from resonant targets embedded in foliage (foliage penetrating radar); in these examples we extract late-time target resonant frequencies. We have also applied superresolution algorithms to wavefront-based processing, and results are presented here for model targets.

A fundamental challenge to Remote Sensing is mapping the ocean floor in coastal shallow waters where variability, due to the interaction between the coast and the sea, can bring significant disparity in the optical properties of the water column. The objects to be detected, coral reefs, sands and submerged aquatic vegetation, have weak signals, with temporal and spatial variation. In real scenarios the absorption and backscattering coefficients have spatial variation due to different sources of variability (river discharge, different depths of shallow waters, water currents) and temporal fluctuations. This paper presents the development of algorithms for retrieving information and its application to the recognition, classification and mapping of objects under coastal shallow waters. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. The retrieval of information requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. The algorithms developed were applied to one set of remotely sensed data: a high resolution HYPERION hyperspectral imagery. An inverse problem arises as this spectral data is used for mapping the ocean shallow waters floor. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo of the ocean floor using a priori information in the form of stored spectral signatures, previously measured, of objects of interest, such as sand, corals, and sea grass.

On long-duration missions to other planets astronauts will be exposed to types and doses of radiation that are not experienced in low earth orbit. Previous research using a ground-based model for exposure to cosmic rays has shown that exposure to heavy particles, such as 56Fe, disrupts spatial learning and memory measured using the Morris water maze. Maintaining rats on diets containing antioxidant phytochemicals for 2 weeks prior to irradiation ameliorated this deficit. The present experiments were designed to determine: (1) the generality of the particle-induced disruption of memory by examining the effects of exposure to 56Fe particles on objectrecognition memory; and (2) whether maintaining rats on these antioxidant diets for 2 weeks prior to irradiation would also ameliorate any potential deficit. The results showed that exposure to low doses of 56Fe particles does disrupt recognition memory and that maintaining rats on antioxidant diets containing blueberry and strawberry extract for only 2 weeks was effective in ameliorating the disruptive effects of irradiation. The results are discussed in terms of the mechanisms by which exposure to these particles may produce effects on neurocognitive performance.

We have proposed in this paper an embedded palmprint recognitionsystem using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the central pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance. PMID:22438721

We have proposed in this paper an embedded palmprint recognitionsystem using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance. PMID:22438721

We examined the influence of print exposure on the body-object interaction (BOI) effect in visual word recognition. High print exposure readers and low print exposure readers either made semantic categorizations (“Is the word easily imageable?”; Experiment 1) or phonological lexical decisions (“Does the item sound like a real English word?”; Experiment 2). The results from Experiment 1 showed that there was a larger BOI effect for the low print exposure readers than for the high print exposure readers in semantic categorization, though an effect was observed for both print exposure groups. However, the results from Experiment 2 showed that the BOI effect was observed only for the high print exposure readers in phonological lexical decision. The results of the present study suggest that print exposure does influence the BOI effect, and that this influence varies as a function of task demands. PMID:22563312

A system and method for disrupting at least one component of a suspect object is provided. The system includes a source for passing radiation through the suspect object, a screen for receiving the radiation passing through the suspect object and generating at least one image therefrom, a weapon having a discharge deployable therefrom, and a targeting unit. The targeting unit displays the image(s) of the suspect object and aims the weapon at a disruption point on the displayed image such that the weapon may be positioned to deploy the discharge at the disruption point whereby the suspect object is disabled.

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognitionsystems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognitionsystem due to its accuracy and efficiency. PMID:26346654

The review examines six personal computer-based optical character recognition (OCR) systems designed for use by blind and visually impaired people. Considered are OCR components and terms, documentation, scanning and reading, command structure, conversion, unique features, accuracy of recognition, scanning time, speed, and cost. (DB)

To go beyond qualitative models of the biological substrate of objectrecognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core objectrecognition performance over a broad range of tasks? We measured human performance in 64 objectrecognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie objectrecognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each objectrecognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual objectrecognition and determine whether a single neuronal linking hypothesis can quantitatively account for core objectrecognition behavior. To achieve this, we designed a

To go beyond qualitative models of the biological substrate of objectrecognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core objectrecognition performance over a broad range of tasks? We measured human performance in 64 objectrecognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie objectrecognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each objectrecognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual objectrecognition and determine whether a single neuronal linking hypothesis can quantitatively account for core objectrecognition behavior. To achieve this, we designed a

This study proposed a new framework that combines pixel-level change detection and object-level recognition to detect changes of built-up land from high-spatial resolution remote sensing images. First, an adaptive differencing method was designed to detect changes at the pixel level based on both spectral and textural features. Next, the changed pixels were subjected to a set of morphological operations to improve the completeness and to generate changed objects, achieving the transition of change detection from the pixel level to the object level. The changed objects were further recognised through the difference of morphological building index in two phases to indicate changed objects on built-up land. The transformation from changed pixels to changed objects makes the proposed framework distinct with both the pixel-based and the object-based change detection methods. Compared with the pixel-based methods, the proposed framework can improve the change detection capability through the transformation and successive recognition of objects. Compared with the object-based method, the proposed framework avoids the issue of multitemporal segmentation and can generate changed objects directly from changed pixels. The experimental results show the effectiveness of the transformation from changed pixels to changed objects and the successive object-based recognition on improving the detection accuracy, which justify the application potential of the proposed change detection framework.

Objective Previously, four months of a blueberry-enriched (BB) antioxidant diet prevented impaired objectrecognition memory in aged rats. Experiment 1 determined whether one and two-month BB diets would have a similar effect and whether the benefits would disappear promptly after terminating the d...

We propose a high security facial recognitionsystem using a cellular phone on the mobile network. This system is composed of a face recognition engine based on optical phase correlation which uses phase information with emphasis on a Fourier domain, a control sever and the cellular phone with a compact camera for taking pictures, as a portable terminal. Compared with various correlation methods, our face recognition engine revealed the most accurate EER of less than 1%. By using the JAVA interface on this system, we implemented the stable system taking pictures, providing functions to prevent spoofing while transferring images. This recognitionsystem was tested on 300 women students and the results proved this system effective.

The digitization of ancient Chinese documents presents new challenges to OCR (Optical Character Recognition) research field due to the large character set of ancient Chinese characters, variant font types, and versatile document layout styles, as these documents are historical reflections to the thousands of years of Chinese civilization. After analyzing the general characteristics of ancient Chinese documents, we present a solution for recognition of ancient Chinese documents with regular font-types and layout-styles. Based on the previous work on multilingual OCR in TH-OCR system, we focus on the design and development of two key technologies which include character recognition and page segmentation. Experimental results show that the developed character recognition kernel of 19,635 Chinese characters outperforms our original traditional Chinese recognition kernel; Benchmarked test on printed ancient Chinese books proves that the proposed system is effective for regular ancient Chinese documents.

Introduces a remote weapon station basic composition and the main advantage, analysis of target based on image automatic recognitionsystem for remote weapon station of practical significance, the system elaborated the image based automatic target recognitionsystem in the photoelectric stabilized technology, multi-sensor image fusion technology, integrated control target image enhancement, target behavior risk analysis technology, intelligent based on the character of the image automatic target recognition algorithm research, micro sensor technology as the key technology of the development in the field of demand.

For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based

A self amplifying optical pattern recognizer includes a geometric system configuration similar to that of a Vander Lugt holographic matched filter configuration with a photorefractive crystal specifically oriented with respect to the input beams. An extraordinarily polarized, spherically converging object image beam is formed by laser illumination of an input object image and applied through a photorefractive crystal, such as a barium titanite (BaTiO.sub.3) crystal. A volume or thin-film dif ORIGIN OF THE INVENTION The invention described herein was made in the performance of work under a NASA contract, and is subject to the provisions of Public Law 96-517 (35 USC 202) in which the Contractor has elected to retain title.

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees' flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots. PMID:26886006

Honeybees (Apis mellifera) discriminate multiple object features such as colour, pattern and 2D shape, but it remains unknown whether and how bees recover three-dimensional shape. Here we show that bees can recognize objects by their three-dimensional form, whereby they employ an active strategy to uncover the depth profiles. We trained individual, free flying honeybees to collect sugar water from small three-dimensional objects made of styrofoam (sphere, cylinder, cuboids) or folded paper (convex, concave, planar) and found that bees can easily discriminate between these stimuli. We also tested possible strategies employed by the bees to uncover the depth profiles. For the card stimuli, we excluded overall shape and pictorial features (shading, texture gradients) as cues for discrimination. Lacking sufficient stereo vision, bees are known to use speed gradients in optic flow to detect edges; could the bees apply this strategy also to recover the fine details of a surface depth profile? Analysing the bees’ flight tracks in front of the stimuli revealed specific combinations of flight maneuvers (lateral translations in combination with yaw rotations), which are particularly suitable to extract depth cues from motion parallax. We modelled the generated optic flow and found characteristic patterns of angular displacement corresponding to the depth profiles of our stimuli: optic flow patterns from pure translations successfully recovered depth relations from the magnitude of angular displacements, additional rotation provided robust depth information based on the direction of the displacements; thus, the bees flight maneuvers may reflect an optimized visuo-motor strategy to extract depth structure from motion signals. The robustness and simplicity of this strategy offers an efficient solution for 3D-object-recognition without stereo vision, and could be employed by other flying insects, or mobile robots. PMID:26886006

Performance on many memory tests varies across the day and is severely impaired by disruptions in circadian timing. We developed a noninvasive method to permanently eliminate circadian rhythms in Siberian hamsters (Phodopussungorus) so that we could investigate the contribution of the circadian system to learning and memory in animals that are neurologically and genetically intact. Male and female adult hamsters were rendered arrhythmic by a disruptive phase shift protocol that eliminates cycling of clock genes within the suprachiasmatic nucleus (SCN), but preserves sleep architecture. These arrhythmic animals have deficits in spatial working memory and in long-term objectrecognition memory. In a T-maze, rhythmic control hamsters exhibited spontaneous alternation behavior late in the day and at night, but made random arm choices early in the day. By contrast, arrhythmic animals made only random arm choices at all time points. Control animals readily discriminated novel objects from familiar ones, whereas arrhythmic hamsters could not. Since the SCN is primarily a GABAergic nucleus, we hypothesized that an arrhythmic SCN could interfere with memory by increasing inhibition in hippocampal circuits. To evaluate this possibility, we administered the GABAA antagonist pentylenetetrazole (PTZ; 0.3 or 1.0 mg/kg/day) to arrhythmic hamsters for 10 days, which is a regimen previously shown to produce long-term improvements in hippocampal physiology and behavior in Ts65Dn (Down syndrome) mice. PTZ restored long-term objectrecognition and spatial working memory for at least 30 days after drug treatment without restoring circadian rhythms. PTZ did not augment memory in control (entrained) animals, but did increase their activity during the memory tests. Our findings support the hypothesis that circadian arrhythmia impairs declarative memory by increasing the relative influence of GABAergic inhibition in the hippocampus. PMID:24009680

The proposed method given in this article is prepared for analysis of data in the form of cloud of points directly from 3D measurements. It is designed for use in the end-user applications that can directly be integrated with 3D scanning software. The method utilizes locally calculated feature vectors (FVs) in point cloud data. Recognition is based on comparison of the analyzed scene with reference object library. A global descriptor in the form of a set of spatially distributed FVs is created for each reference model. During the detection process, correlation of subsets of reference FVs with FVs calculated in the scene is computed. Features utilized in the algorithm are based on parameters, which qualitatively estimate mean and Gaussian curvatures. Replacement of differentiation with averaging in the curvatures estimation makes the algorithm more resistant to discontinuities and poor quality of the input data. Utilization of the FV subsets allows to detect partially occluded and cluttered objects in the scene, while additional spatial information maintains false positive rate at a reasonably low level.

Some models of objectrecognition propose that items from structurally crowded categories (e.g., living things) permit faster access to superordinate semantic information than structurally dissimilar categories (e.g., nonliving things), but slower access to individual object information when naming items. We present four experiments that utilize the same matched stimuli: two examine superordinate categorization and two examine picture naming. Experiments 1 and 2 required participants to sort pictures into their appropriate superordinate categories and both revealed faster categorization for living than nonliving things. Nonetheless, the living thing superiority disappeared when the atypical categories of body parts and musical instruments were excluded. Experiment 3 examined naming latency and found no difference between living and nonliving things. This finding was replicated in Experiment 4 where the same items were presented in different formats (e.g., color and line-drawn versions). Taken as a whole, these experiments show that the ease with which people categorize items maps strongly onto the ease with which they name them. PMID:16377049

A system and method are provided for recording density changes in a flow field surrounding a moving object. A mask having an aperture for regulating the passage of images therethrough is placed in front of an image recording medium. An optical system is placed in front of the mask. A transition having a light field-of-view and a dark field-of-view is located beyond the test object. The optical system focuses an image of the transition at the mask such that the aperture causes a band of light to be defined on the image recording medium. The optical system further focuses an image of the object through the aperture of the mask so that the image of the object appears on the image recording medium. Relative motion is minimized between the mask and the transition. Relative motion is also minimized between the image recording medium and the image of the object. In this way, the image of the object and density changes in a flow field surrounding the object are recorded on the image recording medium when the object crosses the transition in front of the optical system.

"Nr4a1" and "Nr4a2" are transcription factors and immediate early genes belonging to the nuclear receptor Nr4a family. In this study, we examine their role in long-term memory formation for object location and objectrecognition. Using siRNA to block expression of either "Nr4a1" or "Nr4a2", we found that "Nr4a2" is necessary for both long-term…

Objectives Anaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognitionsystems were combined to design and realize a new hybrid-type anaphora recognitionsystem with an optimum capacity. Methods Anaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognitionsystem by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognitionsystem. Results The precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method. Conclusions The hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods. PMID:25405063

This report describes an automatic target recognitionsystem for fast screening of large amounts of multi-sensor image data, based on low-cost parallel processors. This system uses image data fusion and gives uncertainty estimates. It is relatively low cost, compact, and transportable. The software is easily enhanced to expand the system`s capabilities, and the hardware is easily expandable to increase the system`s speed. This volume is a user`s manual for an Automatic Target Recognition (ATR) system. This guide is intended to provide enough information and instruction to allow individuals to the system for their own applications.

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

The spontaneous response to novelty is the basis of one-trial objectrecognition tests for the study of objectrecognition memory (ORM) in rodents. We describe an objectrecognition task for the rabbit, based on its natural tendency to scent-mark ("chin") novel objects. The objectrecognition task comprised a 15min sample phase in which the rabbit was placed into an open field arena containing two similar objects, then removed for a 5-360min delay, and then returned to the same arena that contained one object similar to the original ones ("Familiar") and one that differed from the original ones ("Novel"), for a 15min test phase. Chin-marks directed at each of the objects were registered. Some animals received injections (sc) of saline, ketamine (1mg/kg), or MK-801 (37μg/kg), 5 or 20min before the sample phase. We found that chinning decreased across the sample phase, and that this response showed stimulus specificity, a defining characteristic of habituation: in the test phase, chinning directed at the Novel, but not Familiar, object was increased. Chinning directed preferentially at the novel object, which we interpret as novelty-induced sensitization and the behavioral correlate of ORM, was promoted by tactile/visual and spatial novelty. ORM deficits were induced by pre-treatment with MK-801 and, to a lesser extent, ketamine. Novel object discrimination was not observed after delays longer than 5min. These results suggest that short-term habituation and sensitization, not long-term memory, underlie novel object discrimination in this test paradigm. PMID:23651879

The Institute for Astronomy at the University of Hawaii is developing a large optical astronomical surveying system - the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). The Moving Object Processing System (MOPS) client of the Pan-STARRS image processing pipeline is developing software to automatically discover and identify >90% of near-Earth objects (NEOs) 300m in diameter and >80% of other classes of asteroids and comets. In developing its software, MOPS has created a synthetic solar system model (SSM) with over 10 million objects whose distributions of orbital characteristics matches those expected for objects that Pan-STARRS will observe. MOPS verifies its correct operation by simulating the survey and subsequent discovery of synthetically generated objects. MOPS also employs novel techniques in handling the computationally difficult problem of linking large numbers of unknown asteroids in a field of detections. We will describe the creation and verification of the Pan-STARRS MOPS SSM, demonstrate synthetic detections and observations by the MOPS, describe the MOPS asteroid linking techniques, describe accuracy and throughput of the entire MOPS system, and provide predictions regarding the numbers and kinds of objects, including as yet undiscovered "extreme objects", that the MOPS expects to find over its 10-year lifetime. Pan-STARRS is funded under a grant from the U.S. Air Force.

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and objectrecognition algorithms for video surveillance systems. PMID:27347978

Recent studies suggest that visual objectrecognition is a proactive process through which perceptual evidence accumulates over time before a decision can be made about the object. However, the exact electrophysiological correlates and time-course of this complex process remain unclear. In addition, the potential influence of emotion on this process has not been investigated yet. We recorded high density EEG in healthy adult participants performing a novel perceptual recognition task. For each trial, an initial blurred visual scene was first shown, before the actual content of the stimulus was gradually revealed by progressively adding diagnostic high spatial frequency information. Participants were asked to stop this stimulus sequence as soon as they could correctly perform an animacy judgment task. Behavioral results showed that participants reliably gathered perceptual evidence before recognition. Furthermore, prolonged exploration times were observed for pleasant, relative to either neutral or unpleasant scenes. ERP results showed distinct effects starting at 280 ms post-stimulus onset in distant brain regions during stimulus processing, mainly characterized by: (i) a monotonic accumulation of evidence, involving regions of the posterior cingulate cortex/parahippocampal gyrus, and (ii) true categorical recognition effects in medial frontal regions, including the dorsal anterior cingulate cortex. These findings provide evidence for the early involvement, following stimulus onset, of non-overlapping brain networks during proactive processes eventually leading to visual objectrecognition. PMID:21237274

Objective The recognition of the limits between normal and pathological aging is essential to start preventive actions. The aim of this paper is to compare the Cambridge Neuropsychological Test Automated Battery (CANTAB) and language tests to distinguish subtle differences in cognitive performances in two different age groups, namely young adults and elderly cognitively normal subjects. Method We selected 29 young adults (29.9±1.06 years) and 31 older adults (74.1±1.15 years) matched by educational level (years of schooling). All subjects underwent a general assessment and a battery of neuropsychological tests, including the Mini Mental State Examination, visuospatial learning, and memory tasks from CANTAB and language tests. Cluster and discriminant analysis were applied to all neuropsychological test results to distinguish possible subgroups inside each age group. Results Significant differences in the performance of aged and young adults were detected in both language and visuospatial memory tests. Intragroup cluster and discriminant analysis revealed that CANTAB, as compared to language tests, was able to detect subtle but significant differences between the subjects. Conclusion Based on these findings, we concluded that, as compared to language tests, large-scale application of automated visuospatial tests to assess learning and memory might increase our ability to discern the limits between normal and pathological aging. PMID:25565785

Dietary modifications have been shown to contribute to the physical and mental diseases. Oxidative modifications of protein can be easily found in protein-rich food such as meat and milk products. Previous studies mainly focus on the consequences of lipid oxidation products intake in vivo, but the effects of protein oxidation products consumption have been largely neglected. Oxidants have been shown to play an important role in aging and neurodegenerative diseases. Dityrosine is the oxidated product of tyrosine residues in protein which is considered as a biomarker for oxidative stress, but the potential deleterious effects of dityrosine are unknown. In the present study, we explored the effects of dityrosine administration on the behavioral aspect. We found that dityrosine-ingested mice displayed impaired memory during novel objectrecognition test, but no influence to the spatial memory in Morris water maze compared with the saline group. Other aspects of neurobehavioral function such as locomotor activity, anxiety and social behavior were not affected by dityrosine ingestion. Furthermore, we found that dityrosine-ingested mice showed decreased expression level of NMDA receptor subunits Nr1, Nr2a, Nr2b as well as Bdnf, Trkb. Our study suggests that dityrosine exposure impairs hippocampus-dependent nonspatial memory accompanied by modulation of NMDA receptor subunits and Bdnf expression. PMID:27317839

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual objectrecognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual objectrecognition in the human visual brain. PMID:27282108

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual objectrecognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual objectrecognition in the human visual brain. PMID:27282108

Computer based pattern recognition is a process that involves several sub-processes, including pre-processing, feature extraction, feature selection, and classification. Feature extraction is the estimation of certain attributes of the target patterns. Selection of the right set of features is the most crucial and complex part of building a pattern recognitionsystem. In this work we have combined multiple features extracted using seven different approaches. The novelty of this approach is to achieve better accuracy and reduced computational time for recognition of handwritten characters using Genetic Algorithm which optimizes the number of features along with a simple and adaptive Multi Layer Perceptron classifier. Experiments have been performed using standard database of CEDAR (Centre of Excellence for Document Analysis and Recognition) for English alphabet. The experimental results obtained on this database demonstrate the effectiveness of this system. PMID:27066370

We have constructed an optical correlator for fast face recognition. Recognition rate can be markedly improved, if reference images are optically recorded and can be accessed directly without converting them to digital signals. In addition, a large capacity of optical storage allows us to increase the size of the reference database. We propose a new optical correlator that integrates the optical correlation technology used in our face recognitionsystem and collinear holography. From preliminary correlation experiments using the collinear optical set-up, we achieved excellent performance of high correlation peaks and low error rates. We expect an optical correlation of 10 μs/frame, i.e., 100,000 face/s when applied to face recognition. This system can also be applied to various image searches.

Describes the use of object technology for the development of information management systems. Notes the benefits of modelling complex real-world systems and the increases in productivity leading to flexible, reusable, and maintainable software. Discusses problems, support for multimedia data types, and storage capabilities. (AEF)

We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit. PMID:25122896

We challenge the claim that there are distinct neural systems for explicit and implicit memory by demonstrating that a formal single-system model predicts the pattern of recognition memory (explicit) and repetition priming (implicit) in amnesia. In the current investigation, human participants with amnesia categorized pictures of objects at study and then, at test, identified fragmented versions of studied (old) and nonstudied (new) objects (providing a measure of priming), and made a recognition memory judgment (old vs new) for each object. Numerous results in the amnesic patients were predicted in advance by the single-system model, as follows: (1) deficits in recognition memory and priming were evident relative to a control group; (2) items judged as old were identified at greater levels of fragmentation than items judged new, regardless of whether the items were actually old or new; and (3) the magnitude of the priming effect (the identification advantage for old vs new items) overall was greater than that of items judged new. Model evidence measures also favored the single-system model over two formal multiple-systems models. The findings support the single-system model, which explains the pattern of recognition and priming in amnesia primarily as a reduction in the strength of a single dimension of memory strength, rather than a selective explicit memory system deficit. PMID:25122896

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus. PMID:22723777

This study compared the effect of stimulus inversion on 3- to 5-year-olds' recognition of faces and two nonface object categories matched with faces for a number of attributes: shoes (Experiment 1) and frontal images of cars (Experiments 2 and 3). The inversion effect was present for faces but not shoes at 3 years of age (Experiment 1). Analogous…

Recognition memory, involving the ability to discriminate between a novel and familiar object, depends on the integrity of the perirhinal cortex (PRH). Glutamate, the main excitatory neurotransmitter in the cortex, is essential for many types of memory processes. Of the subtypes of glutamate receptor, metabotropic receptors (mGluRs) have received…

The mammalian target of rapamycin (mTOR) signaling pathway is an important regulator of protein synthesis and is essential for various forms of hippocampal memory. Here, we asked whether the enhancement of objectrecognition memory consolidation produced by dorsal hippocampal infusion of 17[Beta]-estradiol (E[subscript 2]) is dependent on mTOR…

We examined the effects of sensorimotor experience in two visual word recognition tasks. Body-object interaction (BOI) ratings were collected for a large set of words. These ratings assess perceptions of the ease with which a human body can physically interact with a word's referent. A set of high BOI words (e.g., "mask") and a set of low BOI…

Experience-dependent plasticity in deaf participants has been shown in a variety of studies focused on either the dorsal or ventral aspects of the visual system, but both systems have never been investigated in concert. Using functional magnetic resonance imaging (fMRI), we investigated functional plasticity for spatial processing (a dorsal visual pathway function) and for object processing (a ventral visual pathway function) concurrently, in the context of differing sensory (auditory deprivation) and language (use of a signed language) experience. During scanning, deaf native users of American Sign Language (ASL), hearing native ASL users, and hearing participants without ASL experience attended to either the spatial arrangement of frames containing objects or the identity of the objects themselves. These two tasks revealed the expected dorsal/ventral dichotomy for spatial versus object processing in all groups. In addition, the object identity matching task contained both face and house stimuli, allowing us to examine category-selectivity in the ventral pathway in all three participant groups. When contrasting the groups we found that deaf signers differed from the two hearing groups in dorsal pathway parietal regions involved in spatial cognition, suggesting sensory experience-driven plasticity. Group differences in the object processing system indicated that responses in the face-selective right lateral fusiform gyrus and anterior superior temporal cortex were sensitive to a combination of altered sensory and language experience, whereas responses in the amygdala were more closely tied to sensory experience. By selectively engaging the dorsal and ventral visual pathways within participants in groups with different sensory and language experiences, we have demonstrated that these experiences affect the function of both of these systems, and that certain changes are more closely tied to sensory experience, while others are driven by the combination of sensory and

Experience-dependent plasticity in deaf participants has been shown in a variety of studies focused on either the dorsal or ventral aspects of the visual system, but both systems have never been investigated in concert. Using functional magnetic resonance imaging (fMRI), we investigated functional plasticity for spatial processing (a dorsal visual pathway function) and for object processing (a ventral visual pathway function) concurrently, in the context of differing sensory (auditory deprivation) and language (use of a signed language) experience. During scanning, deaf native users of American Sign Language (ASL), hearing native ASL users, and hearing participants without ASL experience attended to either the spatial arrangement of frames containing objects or the identity of the objects themselves. These two tasks revealed the expected dorsal/ventral dichotomy for spatial versus object processing in all groups. In addition, the object identity matching task contained both face and house stimuli, allowing us to examine category-selectivity in the ventral pathway in all three participant groups. When contrasting the groups we found that deaf signers differed from the two hearing groups in dorsal pathway parietal regions involved in spatial cognition, suggesting sensory experience-driven plasticity. Group differences in the object processing system indicated that responses in the face-selective right lateral fusiform gyrus and anterior superior temporal cortex were sensitive to a combination of altered sensory and language experience, whereas responses in the amygdala were more closely tied to sensory experience. By selectively engaging the dorsal and ventral visual pathways within participants in groups with different sensory and language experiences, we have demonstrated that these experiences affect the function of both of these systems, and that certain changes are more closely tied to sensory experience, while others are driven by the combination of sensory and

JeoViewer is an intelligent object-oriented geographic information system (GIS) framework written in Java that provides transparent linkage to any objects data, behaviors, and optimized spatial geometry representation. Tools are provided for typical GIS functionality, data ingestion, data export, and integration with other frameworks. The primary difference between Jeo Viewer and traditional GIS systems is that traditional GIS systems offer static views of geo-spatial data while JeoViewer can be dynamically coupled to models and live datamore » streams which dynamically change the state of the object which can be immediately represented in JeoViewer. Additionally, JeoViewers object-oriented paradigm provides a more natural representation of spatial data. A rich layer hierarchy allows arbitrary grouping of objects based on any relationship as well as the traditional GIS vertical ordering of objects. JeoViewer can run as a standalone product, extended with additional analysis functionality, or embedded in another framework.« less

JeoViewer is an intelligent object-oriented geographic information system (GIS) framework written in Java that provides transparent linkage to any objects data, behaviors, and optimized spatial geometry representation. Tools are provided for typical GIS functionality, data ingestion, data export, and integration with other frameworks. The primary difference between Jeo Viewer and traditional GIS systems is that traditional GIS systems offer static views of geo-spatial data while JeoViewer can be dynamically coupled to models and live data streams which dynamically change the state of the object which can be immediately represented in JeoViewer. Additionally, JeoViewers object-oriented paradigm provides a more natural representation of spatial data. A rich layer hierarchy allows arbitrary grouping of objects based on any relationship as well as the traditional GIS vertical ordering of objects. JeoViewer can run as a standalone product, extended with additional analysis functionality, or embedded in another framework.

A security system using biometric person authentication technologies is suited to various high-security situations. The technology based on face recognition has advantages such as lower user’s resistance and lower stress. However, facial appearances change according to facial pose, expression, lighting, and age. We have developed the FACELOCK security system based on our face recognition methods. Our methods are robust for various facial appearances except facial pose. Our system consists of clients and a server. The client communicates with the server through our protocol over a LAN. Users of our system do not need to be careful about their facial appearance.

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Occam's razor is often used in science to define the minimum criteria to establish a physical or philosophical idea or relationship. Albert Einstein is attributed the saying "everything should be made as simple as possible, but not simpler". These heuristic ideas are based on a belief that there is a minimum state or set of states for a given system or phenomena. In looking at system complexity, these heuristics point us to an idea that complexity can be reduced to a minimum. How then, do we approach a reduction in complexity? Complexity has been described as a subjective concept and an objective measure of a system. Subjective complexity is based on human cognitive comprehension of the functions and inter relationships of a system. Subjective complexity is defined by the ability to fully comprehend the system. Simplifying complexity, in a subjective sense, is thus gaining a deeper understanding of the system. As Apple's Jonathon Ive has stated," It's not just minimalism or the absence of clutter. It involves digging through the depth of complexity. To be truly simple, you have to go really deep". Simplicity is not the absence of complexity but a deeper understanding of complexity. Subjective complexity, based on this human comprehension, cannot then be discerned from the sociological concept of ignorance. The inability to comprehend a system can be either a lack of knowledge, an inability to understand the intricacies of a system, or both. Reduction in this sense is based purely on a cognitive ability to understand the system and no system then may be truly complex. From this view, education and experience seem to be the keys to reduction or eliminating complexity. Objective complexity, is the measure of the systems functions and interrelationships which exist independent of human comprehension. Jonathon Ive's statement does not say that complexity is removed, only that the complexity is understood. From this standpoint, reduction of complexity can be approached

A generalized application area of machine vision is in the classification of different objects based on specified criteria. Applications of this nature are encountered more and more often in real industrial situations and the need to design robust classification architectures is now being felt more intensely than ever before. In designing such systems, it is being increasingly realized that judicious combination of multiple experts forming an integral configuration can achieve a higher overall performance than any of the individual experts on its own. Many configurations, taking advantage of different individual strengths of different experts, have been investigated. One particular class of structure seeks to exploit the a priori knowledge about the behavior of a particular basic classifier on a particular reference data base and uses that information to form a hierarchical classification structure that treats the structurally similar and dissimilar objects separately. The basic classifier performs an initial separation of the input objects. Based on a priori knowledge, initially separated objects are regrouped to form structurally similar groups, incorporating objects that have a high probability of being confused. A number of such groups having two or three classes in each group can be formed. The structurally dissimilar objects are classified using a generalized classifier. On the other hand, the different groups formed in the previous stage undergo group- wise classification. The final decision of the classifier structure is formed by combining the decisions of the generalized classifier and the specialized group-wise classifiers.

How do we segment and recognize novel objects? When explicit cues from motion and color are available, object boundary detection is relatively easy. However, under conditions of deep camouflage, in which objects share the same image cues as their background, the visual system must reassign new functional roles to existing image statistics in order to group continuities for detection and segmentation of object boundaries. This bootstrapped learning process is stimulus dependent and requires extensive task-specific training. Using a between-subject design, we tested participants on their ability to segment and recognize novel objects after a consolidation period of sleep or wake. We found a specific role for rapid eye movement (REM, n = 43) sleep in context-invariant novel object learning, and that REM sleep as well as a period of active wake (AW, n = 35) increased segmentation of context-specific object learning compared to a period of quiet wake (QW, n = 38; p = .007 and p = .017, respectively). Performance in the non-REM nap group (n = 32) was not different from the other groups. The REM sleep enhancement effect was especially robust for the top performing quartile of subjects, or "super learners" (p = .037). Together, these results suggest that the construction and generalization of novel representations through bootstrapped learning may benefit from REM sleep, and more specific object learning may also benefit from AW. We discuss these results in the context of shared electrophysiological and neurochemical features of AW and REM sleep, which are distinct from QW and non-REM sleep. PMID:24504196

The long-term effects of intermittent ethanol exposure during adolescence (AIE) are of intensive interest and investigation. The effects of AIE on learning and memory and the neural functions that drive them are of particular interest as clinical findings suggest enduring deficits in those cognitive domains in humans after ethanol abuse during adolescence. Although studies of such deficits after AIE hold much promise for identifying mechanisms and therapeutic interventions, the findings are sparse and inconclusive. The present results identify a specific deficit in memory function after AIE and establish a possible neural mechanism of that deficit that may be of translational significance. Male rats (starting at PND-30) received exposure to AIE (5g/kg, i.g.) or vehicle and were allowed to mature into adulthood. At PND-71, one group of animals was assessed using the spatial-temporal objectrecognition (stOR) test to evaluate memory function. A separate group of animals was used to assess the density of cholinergic neurons in forebrain areas Ch1-4 using immunohistochemistry. AIE exposed animals manifested deficits in the temporal component of the stOR task relative to controls, and a significant decrease in the number of ChAT labeled neurons in forebrain areas Ch1-4. These findings add to the growing literature indicating long-lasting neural and behavioral effects of AIE that persist into adulthood and indicate that memory-related deficits after AIE depend upon the tasks employed, and possibly their degree of complexity. Finally, the parallel finding of diminished cholinergic neuron density suggests a possible mechanism underlying the effects of AIE on memory and hippocampal function as well as possible therapeutic or preventive strategies for AIE. PMID:26529506

Forgetting is a universal feature for most types of memories. The best-defined and extensively characterized behaviors that depict forgetting are natural memory decay and interference-based forgetting [1, 2]. Molecular mechanisms underlying the active forgetting remain to be determined for memories in vertebrates. Recent progress has begun to unravel such mechanisms underlying the active forgetting [3-11] that is induced through the behavior-dependent activation of intracellular signaling pathways. In Drosophila, training-induced activation of the small G protein Rac1 mediates natural memory decay and interference-based forgetting of aversive conditioning memory [3]. In mice, the activation of photoactivable-Rac1 in recently potentiated spines in a motor learning task erases the motor memory [12]. These lines of evidence prompted us to investigate a role for Rac1 in time-based natural memory decay and interference-based forgetting in mice. The inhibition of Rac1 activity in hippocampal neurons through targeted expression of a dominant-negative Rac1 form extended objectrecognition memory from less than 72 hr to over 72 hr, whereas Rac1 activation accelerated memory decay within 24 hr. Interference-induced forgetting of this memory was correlated with Rac1 activation and was completely blocked by inhibition of Rac1 activity. Electrophysiological recordings of long-term potentiation provided independent evidence that further supported a role for Rac1 activation in forgetting. Thus, Rac1-dependent forgetting is evolutionarily conserved from invertebrates to vertebrates. PMID:27593377

The long-term effects of intermittent ethanol exposure during adolescence (AIE) are of intensive interest and investigation. The effects of AIE on learning and memory and the neural functions that drive them are of particular interest as clinical findings suggest enduring deficits in those cognitive domains in humans after ethanol abuse during adolescence. Although studies of such deficits after AIE hold much promise for identifying mechanisms and therapeutic interventions, the findings are sparse and inconclusive. The present results identify a specific deficit in memory function after AIE and establish a possible neural mechanism of that deficit that may be of translational significance. Male rats (starting at PND-30) received exposure to AIE (5g/kg, i.g.) or vehicle and were allowed to mature into adulthood. At PND-71, one group of animals was assessed using the spatial-temporal objectrecognition (stOR) test to evaluate memory function. A separate group of animals was used to assess the density of cholinergic neurons in forebrain areas Ch1-4 using immunohistochemistry. AIE exposed animals manifested deficits in the temporal component of the stOR task relative to controls, and a significant decrease in the number of ChAT labeled neurons in forebrain areas Ch1-4. These findings add to the growing literature indicating long-lasting neural and behavioral effects of AIE that persist into adulthood and indicate that memory-related deficits after AIE depend upon the tasks employed, and possibly their degree of complexity. Finally, the parallel finding of diminished cholinergic neuron density suggests a possible mechanism underlying the effects of AIE on memory and hippocampal function as well as possible therapeutic or preventive strategies for AIE. PMID:26529506

Structure-based ligand design in medicinal chemistry and crop protection relies on the identification and quantification of weak noncovalent interactions and understanding the role of water. Small-molecule and protein structural database searches are important tools to retrieve existing knowledge. Thermodynamic profiling, combined with X-ray structural and computational studies, is the key to elucidate the energetics of the replacement of water by ligands. Biological receptor sites vary greatly in shape, conformational dynamics, and polarity, and require different ligand-design strategies, as shown for various case studies. Interactions between dipoles have become a central theme of molecular recognition. Orthogonal interactions, halogen bonding, and amide⋅⋅⋅π stacking provide new tools for innovative lead optimization. The combination of synthetic models and biological complexation studies is required to gather reliable information on weak noncovalent interactions and the role of water. PMID:25630692

A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

Apoptosis is an important process in the development and function of the central nervous system (CNS). To study the role of DNA fragmentation factor 45 (DFF45/ICAD) in CNS function, we previously generated DFF45 knockout mice. We found that whereas they exhibit apparently normal CNS development, DFF45 knockout mice exhibit an increased number of granule cells in the dentate gyrus and enhanced spatial learning and memory compared to wild-type mice in a Morris water maze test. In this study, we examined the performance of the DFF45 knockout mice in a novel objectrecognition task to measure short-term nonspatial memory that is believed to depend on the hippocampal formation. Both wild-type and DFF45 knockout mice exhibited novel objectrecognition 1 h posttraining. However, whereas wild-type mice no longer did so, DFF45 knockout mice were still able to differentiate the novel versus the familiar object 3 h posttraining. The longer memory retention in DFF45 knockout mice did not last up to 24 h as neither wild-type nor DFF45 knockout mice demonstrated novel objectrecognition 24 h posttraining. These results suggest that a lack of DFF45 facilitates hippocampus-dependent nonspatial memory, as well as hippocampus-dependent spatial memory. PMID:12044605

Management and supervision in a management by objectivessystem do not focus on the quality or efficiency of a list of activities. Rather, the manager and supervisor validate progress in reaching agreed outcomes. The implementation of a management and supervision by results approach requires (a) agreement on a statement of mission; (b) agreement…

Discusses the evolution of mainframe and personal computers, and presents a case study of a network developed at the University of Iowa called the Iowa Computer-Aided Engineering Network (ICAEN) that combines Macintosh personal computers with Apollo workstations. Functional objectives are stressed as the best measure of system performance. (LRW)

Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold. PMID:26462936

Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of "pre-filter mechanism", posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an "aggressive-behavior-switching center", where the response is generated if the signal is above a certain threshold. PMID:26462936

One feature of visual processing in the ventral stream is that cortical responses gradually depart from the physical aspects of the visual stimulus and become correlated with perceptual experience. Thus, unlike early retinotopic areas, the responses in the object-related lateral occipital complex (LOC) are typically immune to parameter changes (e.g., contrast, location, etc.) when these do not affect recognition. Here, we use a complementary approach to highlight changes in brain activity following a shift in the perceptual state (in the absence of any alteration in the physical image). Specifically, we focus on LOC and early visual cortex (EVC) and compare their functional magnetic resonance imaging (fMRI) responses to degraded object images, before and after fast perceptual learning that renders initially unrecognized objects identifiable. Using 3 complementary analyses, we find that, in LOC, unlike EVC, learned recognition is associated with a change in the multivoxel response pattern to degraded object images, such that the response becomes significantly more correlated with that evoked by the intact version of the same image. This provides further evidence that the coding in LOC reflects the recognition of visual objects. PMID:24692511

The long-term objective of the effort is to establish successful approaches for 3D acoustic imaging of dense solid objects in air to provide the information required for acquisition and manipulation of these objects by a robotic system. The objective of this first year's work was to achieve and demonstrate the determination of the external geometry (shape) of such objects with a fixed sparse array of sensors, without the aid of geometrical models or extensive training procedures. Conventional approaches for acoustic imaging fall into two basic categories. The first category is used exclusively for dense solid objects. It involves echo-ranging from a large number of sensor positions, achieved either through the use of a larger array of transducers or through extensive physical scanning of a small array. This approach determines the distance to specular reflection points from each sensor position; with suitable processing an image can be inferred. The second category uses the full acoustic waveforms to provide an image, but is strictly applicable only to weak inhomogeneities. The most familiar example is medical imaging of the soft tissue portions of the body where the range of acoustic impedance is relatively small.

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the χ2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy. PMID:25742171

This report describes a personal computer based system for automatic and semiautomatic tracking of objects on film or video tape, developed to meet the needs of the Microgravity Combustion and Fluids Science Research Programs at the NASA Lewis Research Center. The system consists of individual hardware components working under computer control to achieve a high degree of automation. The most important hardware components include 16-mm and 35-mm film transports, a high resolution digital camera mounted on a x-y-z micro-positioning stage, an S-VHS tapedeck, an Hi8 tapedeck, video laserdisk, and a framegrabber. All of the image input devices are remotely controlled by a computer. Software was developed to integrate the overall operation of the system including device frame incrementation, grabbing of image frames, image processing of the object's neighborhood, locating the position of the object being tracked, and storing the coordinates in a file. This process is performed repeatedly until the last frame is reached. Several different tracking methods are supported. To illustrate the process, two representative applications of the system are described. These applications represent typical uses of the system and include tracking the propagation of a flame front and tracking the movement of a liquid-gas interface with extremely poor visibility.

Object positioning system is intended for load transposition. Its main feature is the control method which is based on operator muscle force. One of the most important object position system development problems is a control system design. The article presents the first step of this problem solution that consists of control system structure design.

This Letter investigates and reports on a number of activity recognition methods for a wearable sensor system. The authors apply three methods for data transmission, namely ‘stream-based’, ‘feature-based’ and ‘threshold-based’ scenarios to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. They also report on the impact of variation of sampling frequency and data transmission rate on energy consumption of motes for each method. This study leads us to propose a cross-layer optimisation of an activity recognitionsystem for provisioning acceptable levels of accuracy and energy efficiency. PMID:26609413

Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. PMID:25903553

Driver assistance systems and autonomous robotics rely on the deployment of several sensors for environment perception. Compared to LiDAR systems, the inexpensive vision sensors can capture the 3D scene as perceived by a driver in terms of appearance and depth cues. Indeed, providing 3D image understanding capabilities to vehicles is an essential target in order to infer scene semantics in urban environments. One of the challenges that arises from the navigation task in naturalistic urban scenarios is the detection of road participants (e.g., cyclists, pedestrians and vehicles). In this regard, this paper tackles the detection and orientation estimation of cars, pedestrians and cyclists, employing the challenging and naturalistic KITTI images. This work proposes 3D-aware features computed from stereo color images in order to capture the appearance and depth peculiarities of the objects in road scenes. The successful part-based object detector, known as DPM, is extended to learn richer models from the 2.5D data (color and disparity), while also carrying out a detailed analysis of the training pipeline. A large set of experiments evaluate the proposals, and the best performing approach is ranked on the KITTI website. Indeed, this is the first work that reports results with stereo data for the KITTI object challenge, achieving increased detection ratios for the classes car and cyclist compared to a baseline DPM. PMID:25903553

According to models of objectrecognition in cortex, the brain uses a hierarchical approach in which simple, low-level features having high position and scale specificity are pooled and combined into more complex, higher-level features having greater location invariance. At higher levels, spatial structure becomes implicitly encoded into the features themselves, which may overlap, while explicit spatial information is coded more coarsely. In this paper, the importance of sparsity and localized patch features in a hierarchical model inspired by visual cortex is investigated. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. In order to improve generalization performance, the sparsity is proposed and data dimension is reduced by means of compressive sensing theory and sparse representation algorithm. Similarly, within computational neuroscience, adding the sparsity on the number of feature inputs and feature selection is critical for learning biologically model from the statistics of natural images. Then, a redundancy dictionary of patch-based features that could distinguish object class from other categories is designed and then objectrecognition is implemented by the process of iterative optimization. The method is test on the UIUC car database. The success of this approach suggests a proof for the object class recognition in visual cortex.

The hippocampus is vulnerable to age-dependent memory decline. Multiple forms of memory depend on adequate hippocampal function. Extinction learning comprises active inhibition of no longer relevant learned information concurrent with suppression of a previously learned reaction. It is highly dependent on context, and evidence exists that it requires hippocampal activation. In this study, we addressed whether context-based extinction as well as hippocampus-dependent tasks, such as objectrecognition and object-place recognition, are equally affected by moderate aging. Young (7-8 week old) and older (7-8 month old) Wistar rats were used. For the extinction study, animals learned that a particular floor context indicated that they should turn into one specific arm (e.g., left) to receive a food reward. On the day after reaching the learning criterion of 80% correct choices, the floor context was changed, no reward was given and animals were expected to extinguish the learned response. Both, young and older rats managed this first extinction trial in the new context with older rats showing a faster extinction performance. One day later, animals were returned to the T-maze with the original floor context and renewal effects were assessed. In this case, only young but not older rats showed the expected renewal effect (lower extinction ratio as compared to the day before). To assess general memory abilities, animals were tested in the standard objectrecognition and object-place memory tasks. Evaluations were made at 5 min, 1 h and 7 day intervals. Objectrecognition memory was poor at short-term and intermediate time-points in older but not young rats. Object-place memory performance was unaffected at 5 min, but impaired at 1 h in older but not young rats. Both groups were impaired at 7 days. These findings support that not only aspects of general memory, but also context-dependent extinction learning, are affected by moderate aging. This may reflect less flexibility in

Advanced Optical Systems has developed the world's smallest and lowest cost, fully functional target recognition and tracking system. The heart of the ULTOR target recognition and tracking system is an optical correlator. The system includes real-time preprocessing, large filter stores, filter management logic, correlation detection and thresholding, correlation tracking, and data output. It is self contained, receiving operational commands as an Internet appliance. We will present a demonstration of some of the capabilities of the system using live video signals and real target models. The ULTOR system has wide application in both military and commercial settings. The Navy is considering use of the ULTOR system in several programs, including missile systems and unmanned aerial vehicles.

After a brief description of the robot for picking white asparagus, a statistical study of the different shapes of asparagus tips allowed us to determine certain discriminating parameters to detect the tips as they appear on the silhouette of the mound of earth. The localisation was done stereometrically with the help of two cameras. As the robot carrying the system of vision-localisation moves, the images are altered and decision cri-teria modified. A study of the image from mobile objects produced by both tube and CCD came-ras was carried out. A simulation of this phenomenon has been achieved in order to determine the modifications concerning object shapes, thresholding levels and decision parameters in function of the robot speed.

This study reports on the recognition of different arousal levels, elicited by affective sounds, performed using estimates of autonomic nervous system dynamics. Specifically, as a part of the circumplex model of affect, arousal levels were recognized by properly combining information gathered from standard and nonlinear analysis of heartbeat dynamics, which was derived from the electrocardiogram (ECG). Affective sounds were gathered from the International Affective Digitized Sound System and grouped into four different levels of arousal. A group of 27 healthy volunteers underwent such elicitation while ECG signals were continuously recorded. Results showed that a quadratic discriminant classifier, as applied implementing a leave-one-subject-out procedure, achieved a recognition accuracy of 84.26%. Moreover, this study confirms the crucial role of heartbeat nonlinear dynamics for emotion recognition, hereby estimated through lagged Poincare plots. PMID:26737686

Computerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However, most methods in the literature view this task as a special case of motion recognition. In contrast, we propose to employ the three main components of a physiotherapy exercise (the motion patterns, the stance knowledge, and the exercise object) as different recognition tasks and embed them separately into the recognitionsystem. The low level information about each component is gathered using machine learning methods. Then, we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level, which takes the advantage of domain knowledge for a more robust system. Finally, a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red, green, and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation, bodypart tracking, joint detection, and temporal segmentation methods. In the end, favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained. PMID:24860037

After a commissioning period, the astrometric mission Gaia of the European Space Agency (ESA) started its survey in July 2014. Throughout passed two years the Gaia Data Processing and Analysis Consortium (DPAC) has been treating the data. The current schedule anticipates the first Gaia Data Release (Gaia-DR1) toward the end of summer 2016. Nevertheless, it is not planned to include Solar SystemObjects (SSO) into the first release. This is due to a special treatment required by solar systemobjects, as well as by other peculiar sources (multiple and extended ones). In this presentation, we address issues and recent achivements in SSO processing, in particular validation of SSO-short term data processing chain, GAIA-SSO alerts, as well as the first runs of SSO-long term pipeline.

Most solar systemobjects have never been observed at wavelengths longer than the R band with an angular resolution better than 1". The Hubble Space Telescope itself has only recently been equipped to observe in the infrared. However, because of its small diameter, the angular resolution is lower than that one can now achieved from the ground with adaptive optics, and time allocated to planetary science is limited. We have successfully used adaptive optics on a 4-m class telescope to obtain 0.1" resolution images of solar systemobjects in the far red and near infrared (0.7-2.5 microns), aE wavelengths which best discl"lmlnate their spectral signatures. Our efforts have been put into areas of research for which high angular resolution is essential.

Most solar systemobjects have never been observed at wavelengths longer than the R band with an angular resolution better than 1 sec. The Hubble Space Telescope itself has only recently been equipped to observe in the infrared. However, because of its small diameter, the angular resolution is lower than that one can now achieved from the ground with adaptive optics, and time allocated to planetary science is limited. We have been using adaptive optics (AO) on a 4-m class telescope to obtain 0.1 sec resolution images solar systemobjects at far red and near infrared wavelengths (0.7-2.5 micron) which best discriminate their spectral signatures. Our efforts has been put into areas of research for which high angular resolution is essential, such as the mapping of Titan and of large asteroids, the dynamics and composition of Neptune stratospheric clouds, the infrared photometry of Pluto, Charon, and close satellites previously undetected from the ground.

Recent studies in adult male rats have shown that gonadal hormones influence performance on certain working memory and other types of cognitive tasks that are sensitive to lesions of the medial and/or orbital prefrontal cortices. This study asked whether gonadal hormone modulation of prefrontal cortical function in males also extends to the perirhinal division of the rat prefrontal cortex. Specifically, sham-operated control, gonadectomized, and gonadectomized rats supplemented with testosterone propionate or estradiol were tested on a spontaneous novel objectrecognition task, a paradigm where performance has been shown to be impaired by perirhinal cortical lesions. Using analyses of variance, regression analyses and post-hoc testing to evaluate group differences, it was found that during both the sample and test trials of the task all four groups spent similar absolute and proportional amounts of time ambulating, rearing, stationary, and exploring the two objects present. All groups also explored each of the two identical objects present during sample trials equally. However, during the test trials, only the control and gonadectomized rats given testosterone showed the expected increase in exploration of the novel objects presented, whereas the gonadectomized and gonadectomized, estradiol-supplemental groups continued to explore the novel and familiar objects equally. That regression analyses also identified significant correlations between low bulbospongiosus muscle weight and impaired novel vs. familiar object discrimination further indicates that gonadectomy in adult male rats adversely affects spontaneous novel objectrecognition in an androgen-sensitive, estrogen-insensitive manner. PMID:18511051

In this paper a system for real-time recognition of objects in multidimensional video signals is proposed. Objectrecognition is done by pattern projection into the tensor subspaces obtained from the factorization of the signal tensors representing the input signal. However, instead of taking only the intensity signal the novelty of this paper is first to build the Extended Structural Tensor representation from the intensity signal that conveys information on signal intensities, as well as on higher-order statistics of the input signals. This way the higher-order input pattern tensors are built from the training samples. Then, the tensor subspaces are built based on the Higher-Order Singular Value Decomposition of the prototype pattern tensors. Finally, recognition relies on measurements of the distance of a test pattern projected into the tensor subspaces obtained from the training tensors. Due to high-dimensionality of the input data, tensor based methods require high memory and computational resources. However, recent achievements in the technology of the multi-core microprocessors and graphic cards allows real-time operation of the multidimensional methods as is shown and analyzed in this paper based on real examples of object detection in digital images.

It is a well-established fact that top-down processes influence neural representations in lower-level visual areas. Electrophysiological recordings in monkeys as well as theoretical models suggest that these top-down processes depend on NMDA receptor functioning. However, this underlying neural mechanism has not been tested in humans. We used fMRI multivoxel pattern analysis to compare the neural representations of ambiguous Mooney images before and after they were recognized with their unambiguous grayscale version. Additionally, we administered ketamine, an NMDA receptor antagonist, to interfere with this process. Our results demonstrate that after recognition, the pattern of brain activation elicited by a Mooney image is more similar to that of its easily recognizable grayscale version than to the pattern evoked by the identical Mooney image before recognition. Moreover, recognition of Mooney images decreased mean response; however, neural representations of separate images became more dissimilar. So from the neural perspective, unrecognizable Mooney images all "look the same", whereas recognized Mooneys look different. We observed these effects in posterior fusiform part of lateral occipital cortex and in early visual cortex. Ketamine distorted these effects of recognition, but in early visual cortex only. This suggests that top-down processes from higher- to lower-level visual areas might operate via an NMDA pathway. PMID:25662715

A system is described for monitoring moving objects, such as the flight of honeybees and other insects, using a pulsed laser light source. This system has a self-powered micro-miniaturized transmitting unit powered, in the preferred embodiment, with an array of solar cells. This transmitting unit is attached to the object to be monitored. These solar cells provide current to a storage energy capacitor to produce, for example, five volts for the operation of the transmitter. In the simplest embodiment, the voltage on the capacitor operates a pulse generator to provide a pulsed energizing signal to one or more very small laser diodes. The pulsed light is then received at a receiving base station using substantially standard means which converts the light to an electrical signal for processing in a microprocessor to create the information as to the movement of the object. In the case of a unit for monitoring honeybees and other insects, the transmitting unit weighs less than 50 mg, and has a size no larger than 1[times]3[times]5 millimeters. Also, the preferred embodiment provides for the coding of the light to uniquely identify the particular transmitting unit that is being monitored. A wake-up' circuit is provided in the preferred embodiment whereby there is no transmission until the voltage on the capacitor has exceeded a pre-set threshold. Various other uses of the motion-detection system are described. 4 figures.

A system for monitoring moving objects, such as the flight of honeybees and other insects, using a pulsed laser light source. This system has a self-powered micro-miniaturized transmitting unit powered, in the preferred embodiment, with an array solar cells. This transmitting unit is attached to the object to be monitored. These solar cells provide current to a storage energy capacitor to produce, for example, five volts for the operation of the transmitter. In the simplest embodiment, the voltage on the capacitor operates a pulse generator to provide a pulsed energizing signal to one or more very small laser diodes. The pulsed light is then received at a receiving base station using substantially standard means which converts the light to an electrical signal for processing in a microprocessor to create the information as to the movement of the object. In the case of a unit for monitoring honeybees and other insects, the transmitting unit weighs less than 50 mg, and has a size no larger than 1.times.3.times.5 millimeters. Also, the preferred embodiment provides for the coding of the light to uniquely identify the particular transmitting unit that is being monitored. A "wake-up" circuit is provided in the preferred embodiment whereby there is no transmission until the voltage on the capacitor has exceeded a pre-set threshold. Various other uses of the motion-detection system are described.

It is well established that arousal-induced memory enhancement requires noradrenergic activation of the basolateral complex of the amygdala (BLA) and modulatory influences on information storage processes in its many target regions. While this concept is well accepted, the molecular basis of such BLA effects on neural plasticity changes within other brain regions remains to be elucidated. The present study investigated whether noradrenergic activation of the BLA after objectrecognition training induces chromatin remodeling through histone post-translational modifications in the insular cortex (IC), a brain region that is importantly involved in objectrecognition memory. Male Sprague—Dawley rats were trained on an objectrecognition task, followed immediately by bilateral microinfusions of norepinephrine (1.0 μg) or saline administered into the BLA. Saline-treated control rats exhibited poor 24-h retention, whereas norepinephrine treatment induced robust 24-h objectrecognition memory. Most importantly, this memory-enhancing dose of norepinephrine induced a global reduction in the acetylation levels of histone H3 at lysine 14, H2B and H4 in the IC 1 h later, whereas it had no effect on the phosphorylation of histone H3 at serine 10 or tri-methylation of histone H3 at lysine 27. Norepinephrine administered into the BLA of non-trained control rats did not induce any changes in the histone marks investigated in this study. These findings indicate that noradrenergic activation of the BLA induces training-specific effects on chromatin remodeling mechanisms, and presumably gene transcription, in its target regions, which may contribute to the understanding of the molecular mechanisms of stress and emotional arousal effects on memory consolidation. PMID:25972794

In this paper, we deal with the problem of real-time detection, recognition and tracking of moving objects in open and unknown environments using an infrared (IR) and visible vision system. A thermo-camera and two stereo visible-cameras synchronized are used to acquire multi-source information: three-dimensional data about target geometry and its thermal information are combined to improve the robustness of the tracking procedure. Firstly, target detection is performed by extracting its characteristic features from the images and then by storing the computed parameters on a specific database; secondly, the tracking task is carried on using two different computational approaches. A Hierarchical Artificial Neural Network (HANN) is used during active tracking for the recognition of the actual target, while, when partial occlusions or masking occur, a database retrieval method is used to support the search of the correct target followed. A prototype has been tested on case studies regarding the identification and tracking of animals moving at night in an open environment, and the surveillance of known scenes for unauthorized access control.

Insider problems such as theft and sabotage can occur within the security and surveillance realm of operations when unauthorized people obtain access to sensitive areas. A possible solution to these problems is a means to identify individuals (not just credentials or badges) in a given sensitive area and provide full time personnel accountability. One approach desirable at Department of Energy facilities for access control and/or personnel identification is an Intelligent Facial RecognitionSystem (IFRS) that is non-invasive to personnel. Automatic facial recognition does not require the active participation of the enrolled subjects, unlike most other biological measurement (biometric) systems (e.g., fingerprint, hand geometry, or eye retinal scan systems). It is this feature that makes an IFRS attractive for applications other than access control such as emergency evacuation verification, screening, and personnel tracking. This paper discusses current technology that shows promising results for DOE and other security applications. A survey of research and development in facial recognition identified several companies and universities that were interested and/or involved in the area. A few advanced prototype systems were also identified. Sandia National Laboratories is currently evaluating facial recognitionsystems that are in the advanced prototype stage. The initial application for the evaluation is access control in a controlled environment with a constant background and with cooperative subjects. Further evaluations will be conducted in a less controlled environment, which may include a cluttered background and subjects that are not looking towards the camera. The outcome of the evaluations will help identify areas of facial recognitionsystems that need further development and will help to determine the effectiveness of the current systems for security applications.

The recognition heuristic (RH) is a simple decision strategy that performs surprisingly well in many domains. According to the RH, people decide on the basis of recognition alone and ignore further knowledge when faced with a recognized and an unrecognized choice object. Previous research has revealed noteworthy individual differences in RH use, suggesting that people have preferences for using versus avoiding this strategy that might be causally linked to cognitive or personality traits. However, trying to explain differences in RH use in terms of traits presupposes temporal and cross-situational stability in use of the RH, an important prerequisite that has not been scrutinized so far. In a series of four experiments, we therefore assessed the stability in RH use across (1) time, (2) choice objects, (3) domains, and (4) presentation formats of the choice objects. In Experiment 1, participants worked on the same inference task and choice objects twice, separated by a delay of either one day or one week. Experiment 2 replicated Experiment 1 using two different object sets from the same domain, whereas Experiment 3 assessed the stability of RH use across two different domains. Finally, in Experiment 4 we investigated stability across verbal and pictorial presentation formats of the choice objects. For all measures of RH use proposed so far, we found strong evidence for both temporal and cross-situational stability in use of the RH. Thus, RH use at least partly reflects a person-specific style of decision making whose determinants await further research. PMID:26573057

In the Technology 2006 Case Studies/Success Stories presentation, we will describe and demonstrate a prototype of a compact optical pattern recognitionsystem as an example of a successful technology transfer and continuuing development of state-of-the-art know-how by the close collaboration among government, academia, and small business via the NASA SBIR program. The prototype consists of a complete set of optical pattern recognition hardware with multi-channel storage and retrieval capability that is compactly configured inside a portable 1'X 2'X 3' aluminum case.

Taste sensor as a new kind of chemical sensor has been studied by many researchers. We have developed several types of taste sensor system and some new recognition methods for taste substance. Kiyoshi Toko et al proposed a new kind of chaos taste sensor that is based on sensor chaos dynamics. In this paper, we improve the taste sensor based on chaos dynamics and proposed a new method for the pattern recognition of tastes. We use three kinds of tastes, i.e., sweetness, salty taste, and sourness. They cause the membrane oscillate in different form, and the complexity is not the same. We can detect taste based on the new method.

Hand gesture recognitionsystem has evolved tremendously in the recent few years because of its ability to interact with machine efficiently. Mankind tries to incorporate human gestures into modern technology by searching and finding a replacement of multi touch technology which does not require any touching movement on screen. This paper presents an overview on several methods to realize hand gesture recognition by using three main modules: camera and segmentation module, detection module and feature extraction module. There are many methods which can be used to get the respective results depending on its advantages. Summary of previous research and results of hand gesture methods as well as comparison between gesture recognition are also given in this paper.

In view of the basic Windows login password input way lacking of safety and convenient operation, we will introduce the biometrics technology, face recognition, into the computer to login system. Not only can it encrypt the computer system, also according to the level to identify administrators at all levels. With the enhancement of the system security, user input can neither be a cumbersome nor worry about being stolen password confidential.

In order to increase the speed of photoelectric conversion, a linear CCD is applied as the photoelectric converter instead of the traditional photodiode. A white LED is used as the light source of the system. The color information of the urine test strip is transferred into the CCD through a reflecting optical system. It is then converted to digital signals by an A/D converter. The test results of urine analysis are obtained by a data processing system. An ARM microprocessor is selected as the CPU of the system and a CPLD is employed to provide a driving timing for the CCD drive and the A/D converter. Active HDL7.2 and Verilog HDL are used to simulate the driving timing of the CPLD. Experimental results show that the correctness rate of the test results is better than 90%. The system satisfies the requirements of the color information collection of urine analyzer.

Libraries arise naturally from the increasing complexity of developing scientific applications, the optimization of libraries is just one type of high-performance optimization. Many complex applications areas can today be addressed by domain-specific object-oriented frameworks. Such object-oriented frameworks provide an effective compliment to an object-oriented language and effectively permit the design of what amount to essentially domain-specific languages. The optimization of such a domain-specific library/language combination however is particularly complicated due to the inability of the compiler to optimize the use of the libraries abstractions. The recognition of the use of object-oriented abstractions within user applications is a particularly difficult but important step in the optimization of how objects are used within expressions and statements. Such recognition entails more than just complex pattern matching. The approach presented within this paper uses specially built grammars to parse the C++ representation. The C++ representation is itself obtained using a modified version of the SAGE II C/C++ source code restructuring tool which is inturn based upon the Edison Design Group (EDG) C++ front-end. ROSETTA is a tool which automatically builds grammars and parsers from class definitions, associated parsers parse abstract syntax trees (ASTs) of lower level grammars into ASTs of higher level grammars. The lowest level grammar is that associated with the full C++ language itself, higher level grammars specialize the grammars specific to user defined objects. The grammars form a hierarchy and permit a high-degree of specialization in the recognition of complex use of user defined abstractions.

This interactive, object-oriented, distributed Geographic Information System (GIS) uses the World Wibe Web (WWW) as application medium and distribution mechanism. The software provides distributed access to multiple geo-spatial databases and presents them as if they came from a single coherent database. DOOGIS distributed access comes not only in the form of multiple geo-spatial servers but can break down a single logical server into the constituent physical servers actually storing the data. The program provides formore » dynamic protocol resolution and content handling allowing unknown objects from a particular server to download their handling code. Security and access privileges are negotiated dynamically with each server contacted and each access attempt.« less

Sandia National Laboratories/New Mexicos (SNL/NM) Environmental Management System is the integrated approach for members of the workforce to identify and manage environmental risks. Each Fiscal Year (FY) SNL/NM performs an analysis to identify environmental aspects, and the environmental programs associated with them are charged with the task of routinely monitoring and measuring the objectives and targets that are established to mitigate potential impacts of SNL/NMs operations on the environment. An annual summary of the results achieved towards meeting established Sandia Corporation and SNL/NM Site-specific objectives and targets provides a connection to, and rational for, annually revised environmental aspects. The purpose of this document is to summarize the results achieved and documented in FY2013.

Main Injector Voice Activated Record (MIVAR) system acts on vocal commands and processes spoken inspection data into electronic and printed inspection reports. Devised to improve acquisition and recording of data from borescope inspections of interiors of liquid-oxygen-injecting tubes on main engine of Space Shuttle. With modifications, system used in other situations to relieve inspectors of manual recording of data. Enhances flow of work and quality of data acquired by enabling inspector to remain visually focused on workpiece.

To investigate the role of the perirhinal cortex on the development of recognition measured by the visual paired-comparison (VPC) task, infant monkeys with neonatal perirhinal lesions and sham-operated controls were tested at 1.5, 6, 18, and 48 months of age on the VPC task with color stimuli and intermixed delays of 10 s, 30 s, 60 s, and 120 s. Monkeys with neonatal perirhinal lesions showed an increase in novelty preference between 1.5 and 6 months of age similar to controls, although at these two ages, performance remained significantly poorer than that of control animals. With age, performance in animals with neonatal perirhinal lesions deteriorated as compared to that of controls. In contrast to the lack of novelty preference in monkeys with perirhinal lesions acquired in adulthood, novelty preference in the neonatally operated animals remained above chance at all delays and all ages. The data suggest that, although incidental recognition memory processes can be supported by the perirhinal cortex in early infancy, other temporal cortical areas may support these processes in the absence of a functional perirhinal cortex early in development. The neural substrates mediating incidental recognition memory processes appear to be more widespread in early infancy than in adulthood. PMID:25096364

In a recent Letter, Jupiter is presented as an efficient detector for Ultra-High Energy Cosmic Rays (UHECRs), through measurement by an Earth-orbiting satellite of gamma rays from UHECRs showers produced in Jupiter's atmosphere. We show that this result is incorrect, due to erroneous assumptions on the angular distribution of shower particles. We evaluated other solar systemobjects as potential targets for UHECRs detection, and found that the proposed technique is either not viable or not competitive with traditional ground-based UHECRs detectors.

This study assessed the contribution of edge and surface cues on object representation in macaques (Macaca mulatta). In Experiments 1 and 2, 5 macaques were trained to discriminate 4 simple volumetric objects (geons) and were subsequently tested for their ability to recognize line drawings, silhouettes, and light changes of these geons. Performance was above chance in all test conditions and was similarly high for the line drawings and silhouettes of geons, suggesting the use of the outline shape to recognize the original objects. In addition, transfer for the geons seen under new lighting was greater than for the other stimuli, stressing the importance of the shading information. Experiment 3, using geons filled with new textures, showed that a radical change in the surface cues does not prevent objectrecognition. It is concluded that these findings support a surface-based theory of objectrecognition in macaques, although it does not exclude the contribution of edge cues, especially when surface details are not available. PMID:20141325

In the inspection of cylindrical objects, particularly O-rings, the object is translated through a field of view and a linear light trace is projected on its surface. An image of the light trace is projected on a mask, which has a size and shape corresponding to the size and shape which the image would have if the surface of the object were perfect. If there is a defect, light will pass the mask and be sensed by a detector positioned behind the mask. Preferably, two masks and associated detectors are used, one mask being convex to pass light when the light trace falls on a projection from the surface and the other concave, to pass light when the light trace falls on a depression in the surface. The light trace may be either dynamic, formed by a scanned laser beam, or static, formed by such a beam focussed by a cylindrical lens. Means are provided to automatically keep the illuminating receiving systems properly aligned.

This paper describes a system that exploits the synergy of Hierarchical Mixture Density (HMD) estimation with multiresolution decomposition based hypothesis pruning to perform efficiently joint segmentation and labeling of partially occluded objects in images. First we present the overall structure of the HMD estimation algorithm in the form of a recurrent neural network which generates the posterior probabilities of the various hypotheses associated with the image. Then in order to reduce the large memory and computation requirement we propose a hypothesis pruning scheme making use of the orthonormal discrete wavelet transform for dimensionality reduction. We provide an intuitive justification for the validity of this scheme and present experimental results and performance analysis on real and synthetic images to verify our claims.

Recent work has demonstrated that the perirhinal cortex (PRC) supports conjunctive object representations that aid objectrecognition memory following visual object interference. It is unclear, however, how these representations interact with other brain regions implicated in mnemonic retrieval and how congruent and incongruent interference influences the processing of targets and foils during objectrecognition. To address this, multivariate partial least squares was applied to fMRI data acquired during an interference match-to-sample task, in which participants made object or scene recognition judgments after object or scene interference. This revealed a pattern of activity sensitive to objectrecognition following congruent (i.e., object) interference that included PRC, prefrontal, and parietal regions. Moreover, functional connectivity analysis revealed a common pattern of PRC connectivity across interference and recognition conditions. Examination of eye movements during the same task in a separate study revealed that participants gazed more at targets than foils during correct objectrecognition decisions, regardless of interference congruency. By contrast, participants viewed foils more than targets for incorrect object memory judgments, but only after congruent interference. Our findings suggest that congruent interference makes object foils appear familiar and that a network of regions, including PRC, is recruited to overcome the effects of interference. PMID:25848685

Every language script has its structure, characteristic, and feature. Character based word recognition depends on the feature available to be extracted from character. Word based script recognition overcome the problem of character segmenting and can be applied for several languages (Arabic, Urdu, Farsi... est.). In this paper Arabic handwritten is classified as word based system. Firstly, words segmented and normalized in size to fit the DCT input. Then extract feature characteristic by computing the Euclidean distance between pairs of objects in n-by-m data matrix X. Based on the point's operator of extrema, feature was extracted. Then apply one to one-Class Support Vector Machines (SVMs) as a discriminative framework in order to address feature classification. The approach was tested with several public databases and we get high efficiency rate recognition.

One important principle of object processing is exclusive allocation. Any part of the sensory input, including the border between two objects, can only belong to one object at a time. We tested whether tones forming a spectro-temporal border between two sound patterns can belong to both patterns at the same time. Sequences were composed of low-, intermediate- and high-pitched tones. Tones were delivered with short onset-to-onset intervals causing the high and low tones to automatically form separate low and high sound streams. The intermediate-pitch tones could be perceived as part of either one or the other stream, but not both streams at the same time. Thus these tones formed a pitch ’border’ between the two streams. The tones were presented in a fixed, cyclically repeating order. Linking the intermediate-pitch tones with the high or the low tones resulted in the perception of two different repeating tonal patterns. Participants were instructed to maintain perception of one of the two tone patterns throughout the stimulus sequences. Occasional changes violated either the selected or the alternative tone pattern, but not both at the same time. We found that only violations of the selected pattern elicited the mismatch negativity event-related potential, indicating that only this pattern was represented in the auditory system. This result suggests that individual sounds are processed as part of only one auditory pattern at a time. Thus tones forming a spectro-temporal border are exclusively assigned to one sound object at any given time, as are spatio-temporal borders in vision. PMID:16836636

Performance on tasks requiring discrimination of at least two stimuli can be viewed either from an objective perspective (referring to actual stimulus differences), or from a subjective perspective (corresponding to participant's responses). Using event-related potentials recorded during an old/new recognition memory test involving emotionally laden and neutral words studied either blockwise or randomly intermixed, we show here how the objective perspective (old versus new items) yields late effects of blockwise emotional item presentation at parietal sites that the subjective perspective fails to find, whereas the subjective perspective ("old" versus "new" responses) is more sensitive to early effects of emotion at anterior sites than the objective perspective. Our results demonstrate the potential advantage of dissociating the subjective and the objective perspective onto task performance (in addition to analyzing trials with correct responses), especially for investigations of illusions and information processing biases, in behavioral and cognitive neuroscience studies. PMID:25286129

Objectives Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name recognitionsystem BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination. Design BioTagger-GM consists of four main components: (1) dictionary lookup—gene/protein names in BioThesaurus and biomedical terms in UMLS Metathesaurus are tagged in text, (2) machine learning—machine learning systems are trained using dictionary lookup results as one type of feature, (3) post-processing—heuristic rules are used to correct recognition errors, and (4) system combination—a voting scheme is used to combine recognition results from multiple systems. Measurements The BioCreAtIvE II Gene Mention (GM) corpus was used to evaluate the proposed method. To test its general applicability, the method was also evaluated on the JNLPBA corpus modified for gene/protein name recognition. The performance of the systems was evaluated through cross-validation tests and measured using precision, recall, and F-Measure. Results BioTagger-GM achieved an F-Measure of 0.8887 on the BioCreAtIvE II GM corpus, which is higher than that of the first-place system in the BioCreAtIvE II challenge. The applicability of the method was also confirmed on the modified JNLPBA corpus. Conclusion The results suggest that terminology sources, powerful machine learning frameworks, and system combination can be integrated to build an effective BNER system. PMID:19074302

The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

Primates can easily identify visual objects over large changes in retinal position—a property commonly referred to as position “invariance.” This ability is widely assumed to depend on neurons in inferior temporal cortex (IT) that can respond selectively to isolated visual objects over similarly large ranges of retinal position. However, in the real world, objects rarely appear in isolation, and the interplay between position invariance and the representation of multiple objects (i.e., clutter) remains unresolved. At the heart of this issue is the intuition that the representations of nearby objects can interfere with one another and that the large receptive fields needed for position invariance can exacerbate this problem by increasing the range over which interference acts. Indeed, most IT neurons' responses are strongly affected by the presence of clutter. While external mechanisms (such as attention) are often invoked as a way out of the problem, we show (using recorded neuronal data and simulations) that the intrinsic properties of IT population responses, by themselves, can support objectrecognition in the face of limited clutter. Furthermore, we carried out extensive simulations of hypothetical neuronal populations to identify the essential individual-neuron ingredients of a good population representation. These simulations show that the crucial neuronal property to support recognition in clutter is not preservation of response magnitude, but preservation of each neuron's rank-order object preference under identity-preserving image transformations (e.g., clutter). Because IT neuronal responses often exhibit that response property, while neurons in earlier visual areas (e.g., V1) do not, we suggest that preserving the rank-order object preference regardless of clutter, rather than the response magnitude, more precisely describes the goal of individual neurons at the top of the ventral visual stream. PMID:19439676

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an

Humans can recognize a face with binocular vision, while computers typically use a single face image. It is known that the performance of face recognition (by a computer) can be improved using the score fusion of multimodal images and multiple algorithms. A question is: Can we apply stereo vision to a face recognitionsystem? We know that human binocular vision has many advantages such as stereopsis (3D vision), binocular summation, and singleness of vision including fusion of binocular images (cyclopean image). For face recognition, a 3D face or 3D facial features are typically computed from a pair of stereo images. In human visual processes, the binocular summation and singleness of vision are similar as image fusion processes. In this paper, we propose an advanced face recognitionsystem with stereo imaging capability, which is comprised of two 2-in-1 multispectral (visible and thermal) cameras and three recognition algorithms (circular Gaussian filter, face pattern byte, and linear discriminant analysis [LDA]). Specifically, we present and compare stereo fusion at three levels (images, features, and scores) by using stereo images (from left camera and right camera). Image fusion is achieved with three methods (Laplacian pyramid, wavelet transform, average); feature fusion is done with three logical operations (AND, OR, XOR); and score fusion is implemented with four classifiers (LDA, k-nearest neighbor, support vector machine, binomial logical regression). The system performance is measured by probability of correct classification (PCC) rate (reported as accuracy rate in this paper) and false accept rate (FAR). The proposed approaches were validated with a multispectral stereo face dataset from 105 subjects. Experimental results show that any type of stereo fusion can improve the PCC, meanwhile reduce the FAR. It seems that stereo image/feature fusion is superior to stereo score fusion in terms of recognition performance. Further score fusion after image

A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical objectrecognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the objectrecognition domain.

An experiment was conducted to determine whether a dependent (SR) system would perform with different accuracies given different ways in which it was trained. The experiment used an SR system (Voice Navigator) which is based on Dragon Systems, Inc. (proprietary) technology. Fifteen subjects trained three different voice patterns each and conducted four tests to compile statistics about the recognition accuracy for each pattern. The experiment was successful and demonstrated that the training method used can have significant impact on the performance of a dependent SR system. This thesis discusses the research methodology, reviews and analyzes the data collected, and states conclusions drawn about the particular dependent SR system used in the experiment.

This paper builds up a pattern recognitionsystem to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognitionsystem results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques.

The songs of many insects exhibit precise timing as the result of repetitive and stereotyped subunits on several time scales. As these signals encode the identity of a species, time and timing are important for the recognitionsystem that analyzes these signals. Crickets are a prominent example as their songs are built from sound pulses that are broadcast in a long trill or as a chirped song. This pattern appears to be analyzed on two timescales, short and long. Recent evidence suggests that song recognition in crickets relies on two computations with respect to time; a short linear-nonlinear (LN) model that operates as a filter for pulse rate and a longer integration time window for monitoring song energy over time. Therefore, there is a twofold role for timing. A filter for pulse rate shows differentiating properties for which the specific timing of excitation and inhibition is important. For an integrator, however, the duration of the time window is more important than the precise timing of events. Here, we first review evidence for the role of LN-models and integration time windows for song recognition in crickets. We then parameterize the filter part by Gabor functions and explore the effects of duration, frequency, phase, and offset as these will correspond to differently timed patterns of excitation and inhibition. These filter properties were compared with known preference functions of crickets and katydids. In a comparative approach, the power for song discrimination by LN-models was tested with the songs of over 100 cricket species. It is demonstrated how the acoustic signals of crickets occupy a simple 2-dimensional space for song recognition that arises from timing, described by a Gabor function, and time, the integration window. Finally, we discuss the evolution of recognitionsystems in insects based on simple sensory computations. PMID:25161622

This invention relates to a system and method for weighing articles and quantities of material wherein computing functions are performed to effect calculations and the control of a visual presentation means such as a display or printer or the generation of signals for use in recording a transaction. In particular, the invention relates to such a weighing and computing apparatus and method which operates or varies in response to speech signals generated by selected words of speech spoken into a microphone by an operator of the apparatus. It is known in the art to electronically detect the weight of articles and containers of material and to generate electrical signals which are indicative of such weight. It is also known to effect a computation with respect to such signals and additional signals generated by manually operating selected keys of a keyboard wherein the additional signals represent one or more additional variables which must be divided into or multiplied by the numerical representation of the weights of articles weighed by such apparatus.

GTOSS represents a tether analysis complex which is described by addressing its family of modules. TOSS is a portable software subsystem specifically designed to be introduced into the environment of any existing vehicle dynamics simulation to add the capability of simulating multiple interacting objects (via multiple tethers). These objects may interact with each other as well as with the vehicle into whose environment TOSS is introduced. GTOSS is a stand alone tethered system analysis program, representing an example of TOSS having been married to a host simulation. RTOSS is the Results Data Base (RDB) subsystem designed to archive TOSS simulation results for future display processing. DTOSS is a display post processors designed to utilize the RDB. DTOSS extracts data from the RDB for multi-page printed time history displays. CTOSS is similar to DTOSS, but is designed to create ASCII plot files. The same time history data formats provided for DTOSS (for printing) are available via CTOSS for plotting. How these and other modules interact with each other is discussed.

The present study examined immediate-early gene expression in the perirhinal cortex of rats with hippocampal lesions. The goal was to test those models of recognition memory which assume that the perirhinal cortex can function independently of the hippocampus. The c-fos gene was targeted, as its expression in the perirhinal cortex is strongly associated with recognition memory. Four groups of rats were examined. Rats with hippocampal lesions and their surgical controls were given either a recognition memory task (novel vs. familiar objects) or a relative recency task (objects with differing degrees of familiarity). Perirhinal Fos expression in the hippocampal-lesioned groups correlated with both recognition and recency performance. The hippocampal lesions, however, had no apparent effect on overall levels of perirhinal or entorhinal cortex c-fos expression in response to novel objects, with only restricted effects being seen in the recency condition. Network analyses showed that whereas the patterns of parahippocampal interactions were differentially affected by novel or familiar objects, these correlated networks were not altered by hippocampal lesions. Additional analyses in control rats revealed two modes of correlated medial temporal activation. Novel stimuli recruited the pathway from the lateral entorhinal cortex (cortical layer II or III) to hippocampal field CA3, and thence to CA1. Familiar stimuli recruited the direct pathway from the lateral entorhinal cortex (principally layer III) to CA1. The present findings not only reveal the independence from the hippocampus of some perirhinal systems associated with recognition memory, but also show how novel stimuli engage hippocampal subfields in qualitatively different ways from familiar stimuli. PMID:25264133

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. In this presentation we review the key areas of scientific uncertainty in understanding climate and global change, and follow that with a description of the EOS goals, objectives, and scientific research elements that comprise the program (instrument science teams and interdisciplinary investigations). Finally, I will describe how scientists and policy makers intend to use EOS data improve our understanding of key global change uncertainties, such as: (i) clouds and radiation, including fossil fuel and natural emissions of sulfate aerosol and its potential impact on cloud feedback, (ii) man's impact on ozone depletion, with examples of ClO and O3 obtained from the UARS satellite during the Austral Spring, and (iii) volcanic eruptions and their impact on climate, with examples from the eruption of Mt. Pinatubo.

The Earth Observing System (EOS) is a space-based observing system comprised of a series of satellite sensors by which scientists can monitor the Earth, a Data and Information System (EOSDIS) enabling researchers worldwide to access the satellite data, and an interdisciplinary science research program to interpret the satellite data. In this presentation I will describe the key areas of scientific uncertainty in understanding climate and global change, and follow that with a description of the EOS goals, objectives, and scientific research elements that comprise the program (instrument science teams and interdisciplinary investigations). Finally, I will describe how scientists and policy makers intend to use EOS data to improve our understanding of key global change uncertainties, such as: (i) clouds and radiation, including fossil fuel and natural emissions of sulfate aerosol and its potential impact on cloud feedback, (ii) man's impact on ozone depletion, with examples of ClO and O3 obtained from the UARS satellite during the Austral Spring, and (iii) volcanic eruptions and their impact on climate, with examples from the eruption of Mt. Pinatubo.

The current ear feature matching and recognitionsystem, based on Scale Invariant Feature Transform (SIFT) image matching algorithm, can realize the human ear feature matching and detect the displacement of the human ear so as to reproduce the human ear position and posture. However, due to the influence of image acquisition equipment performance and lighting conditions, too dark or too bright background could bring the locally underexposed or overexposed image. This could result in the loss of some image details so as to make it impossible to identity the image and the recognition rate would be reduced. In this talk, the application of image gray level normalization processing can reduce the sensitivity of imaging to light intensity. Accordingly, it will greatly improve the recognition rate of human ears. Furthermore, it has been found that even if the object is stationary, the image matching results still have certain fluctuation changes, which could be caused by the system error. In order to reduce the error, the Background-based Compensation Model (BCM) has been established based on the investigation of the system error brought by the working environment changes. The results show that, BCM can be used to compensate the system errors of ear recognition matching and further improve the matching accuracy of human ear.

Stress-induced activation of the hypothalamo-pituitary-adrenocortical (HPA) axis and high circulating glucocorticoid levels are well known to impair the retrieval of memory. Vasopressin can activate the HPA axis by stimulating vasopressin 1b (V1b) receptors located on the pituitary. In the present study, we investigated the effect of A-988315, a selective and highly potent non-peptidergic V1b-receptor antagonist with good pharmacokinetic properties, in blocking stress effects on HPA-axis activity and memory retrieval. To study cognitive performance, male Sprague-Dawley rats were trained on an object-discrimination task during which they could freely explore two identical objects. Memory for the objects and their location was tested 24 h later. A-988315 (20 or 60 mg/kg) or water was administered orally 90 min before retention testing, followed 60 min later by stress of footshock exposure. A-988315 dose-dependently dampened stress-induced increases in corticosterone plasma levels, but did not significantly alter HPA-axis activity of non-stressed control rats. Most importantly, A-988315 administration prevented stress-induced impairment of memory retrieval on both the object-recognition and the object-location tasks. A-988315 did not alter the retention of non-stressed rats and did not influence the total time spent exploring the objects or experimental context in either stressed or non-stressed rats. Thus, these findings indicate that direct antagonism of V1b receptors is an effective treatment to block stress-induced activation of the HPA axis and the consequent impairment of retrieval of different aspects of recognition memory. PMID:25669604

Substantial evidence implicates Acetylcholine (ACh) in the acquisition of object memories. While most research has focused on the role of the cholinergic basal forebrain and its cortical targets, there are additional cholinergic networks that may contribute to objectrecognition. The striatum contains an independent cholinergic network comprised of interneurons. In the current study, we investigated the role of this cholinergic signalling in objectrecognition using mice deficient for Vesicular Acetylcholine Transporter (VAChT) within interneurons of the striatum. We tested whether these striatal VAChT(D2-Cre-flox/flox) mice would display normal short-term (5 or 15min retention delay) and long-term (3h retention delay) objectrecognition memory. In a home cage objectrecognition task, male and female VAChT(D2-Cre-flox/flox) mice were impaired selectively with a 15min retention delay. When tested on an object location task, VAChT(D2-Cre-flox/flox) mice displayed intact spatial memory. Finally, when objectrecognition was tested in a Y-shaped apparatus, designed to minimize the influence of spatial and contextual cues, only females displayed impaired recognition with a 5min retention delay, but when males were challenged with a 15min retention delay, they were also impaired; neither males nor females were impaired with the 3h delay. The pattern of results suggests that striatal cholinergic transmission plays a role in the short-term memory for object features, but not spatial location. PMID:27233822

Non-accidental properties (NAPs) correspond to image properties that are invariant to changes in viewpoint (e.g., straight vs. curved contours) and are distinguished from metric properties (MPs) that can change continuously with in-depth object rotation (e.g., aspect ratio, degree of curvature, etc.). Behavioral and electrophysiological studies of shape processing have demonstrated greater sensitivity to differences in NAPs than in MPs. However, previous work has shown that such sensitivity is lacking in multiple-views models of objectrecognition such as Hmax. These models typically assume that object processing is based on populations of view-tuned neurons with distributed symmetrical bell-shaped tuning that are modulated at least as much by differences in MPs as in NAPs. Here, we test the hypothesis that unsupervised learning of invariances to object transformations may increase the sensitivity to differences in NAPs vs. MPs in Hmax. We collected a database of video sequences with objects slowly rotating in-depth in an attempt to mimic sequences viewed during object manipulation by young children during early developmental stages. We show that unsupervised learning yields shape-tuning in higher stages with greater sensitivity to differences in NAPs vs. MPs in agreement with monkey IT data. Together, these results suggest that greater NAP sensitivity may arise from experiencing different in-depth rotations of objects. PMID:26500528

Presents evidence that although patients with semantic deficits can sometimes show good performance on tests or object decisions, this pattern applies when nonsee-objects do not respect the regularities of the domain. Patients with semantic dementia viewed line drawings of a real and chimeric animals side-by-side and were asked to decide which was…

In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognitionsystem on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousands of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.

This paper proposes a novel solution to the problem of scale drift in single-camera simultaneous localization and mapping, based on recognizing and measuring objects. When reconstructing the trajectory of a camera moving in an unknown environment, the scale of the environment, and equivalently the speed of the camera, is obtained by accumulating relative scale estimates over sequences of frames. This leads to scale drift: errors in scale accumulate over time. The proposed solution is to learn the classes of objects that appear throughout the environment and to use measurements of the size of these objects to improve the scale estimate. A bag-of-words-based scheme to learn object classes, to recognize object instances, and to use these observations to correct scale drift is described and is demonstrated reducing accumulated errors by 64% while navigating for 2.5 km through a dynamic outdoor env